Obesity genetic risk model construction method and device based on stacking model and medium

Abstract

The invention provides a stacking model-based obesity genetic risk model construction method, electronic equipment and a storage medium, the method comprises the following steps: obtaining a data set, the data set comprising genetic feature data, and the genetic feature data comprising single nucleotide polymorphism; screening the single nucleotide polymorphisms by using a random forest to obtain a first genetic feature set related to obesity genetic risks; screening the single nucleotide polymorphism by using a distributed gradient enhanced library model to obtain a second genetic feature set related to the obesity genetic risk; obtaining a significant genetic feature set according to the first genetic feature set and the second genetic feature set; constructing an obesity genetic risk model; according to the method, the model evaluation result of the obesity genetic risk model is obtained, the parameters of the obesity genetic risk model are optimized according to the model evaluation result, stacking training is carried out by integrating multiple basic learners and combining meta-classifier logistic regression, the advantages of different algorithms are effectively fused, and the method is suitable for large-scale popularization and application. And the accuracy and the stability of obesity genetic risk prediction are improved.

Description

Technical Field

[0001] The present application relates to the technical field of obesity genetic risk prediction, and in particular to a method for constructing an obesity genetic risk model based on a stacking model, an electronic device, and a storage medium. Background Art

[0002] Obesity is an important public health issue worldwide and has been shown to be closely related to a variety of chronic diseases (such as cardiovascular disease, diabetes, metabolic syndrome, etc.). In recent years, with the development of genomics technology, the genetic basis of obesity has gradually attracted attention. Studies have shown that an individual's susceptibility to obesity is affected by genetic polymorphisms, which usually exist in the form of single nucleotide polymorphisms (SNPs). Although there have been a large number of gene-related obesity studies, how to use this genetic information to accurately assess an individual's risk of obesity remains a major challenge in current scientific research and clinical practice.

[0003] Currently, there is a lack of reliable tools in the relevant technology for assessing the genetic risk of obesity and the risk of death related to obesity. Summary of the invention

[0004] In view of this, it is necessary to provide a method for constructing an obesity genetic risk model based on a stacking model, an electronic device and a storage medium that can overcome at least one of the above defects.

[0005] In a first aspect, an embodiment of the present application provides a method for constructing an obesity genetic risk model based on a stacking model, the method comprising:

[0006] Acquiring a data set, the data set comprising genetic characteristic data, the genetic characteristic data comprising single nucleotide polymorphisms;

[0007] Random forests were applied to screen single nucleotide polymorphisms to obtain the first set of genetic features associated with genetic risk of obesity;

[0008] A distributed gradient boosting library model was applied to screen single nucleotide polymorphisms to obtain a second genetic signature set associated with genetic risk of obesity;

[0009] Obtaining a significant genetic feature set based on the first genetic feature set and the second genetic feature set;

[0010] Constructing an obesity genetic risk model, wherein the obesity genetic risk model is a stacking model, and the obesity genetic risk model includes a basic learner and a meta-classifier;

[0011] The basic learners include: random forest, distributed gradient boosting library model, adaptive boosting and logistic regression;

[0012] Wherein, the algorithms in the basic learner predict the genetic risk of obesity according to the significant genetic feature set, and output the prediction results to the meta-classifier, respectively, and the meta-classifier is used to output the genetic risk prediction results of obesity;

[0013] A model evaluation result of the obesity genetic risk model is obtained, and parameters of the obesity genetic risk model are optimized according to the model evaluation result.

[0014] According to one embodiment of the present application, obtaining a significant genetic feature set according to the first genetic feature set and the second genetic feature set includes:

[0015] The first genetic feature set and the second genetic feature set are intersected to obtain the significant genetic feature set.

[0016] According to one embodiment of the present application, obtaining a significant genetic feature set according to the first genetic feature set and the second genetic feature set further includes:

[0017] The first genetic feature set and the second genetic feature set are analyzed according to lasso regression to obtain the significant genetic feature set.

[0018] According to one embodiment of the present application, the meta-classifier is a logistic regression model, and the meta-classifier generates a genetic risk prediction value for obesity by integrating the prediction results of the basic learner.

[0019] According to an embodiment of the present application, the first genetic feature set obtains the importance score of the genetic feature through a random forest algorithm, and the genetic features with the importance score higher than a preset threshold are screened as the first genetic feature set;

[0020] The second genetic feature set obtains the importance score of the genetic feature through a distributed gradient enhancement library model, and the genetic features with importance scores higher than a preset threshold are screened as the second genetic feature set.

[0021] According to one embodiment of the present application, the method further includes:

[0022] Randomly dividing the significant genetic feature set into five subsets with equal data amounts;

[0023] Applying the four subsets in sequence to train the basic learner, and applying one subset to validate the obesity genetic risk model to form a cross-validation;

[0024] After completing five cross-validations, five sets of prediction values ​​are obtained;

[0025] A training set is formed based on the five sets of predicted values.

[0026] According to one embodiment of the present application, the data set includes an internal test queue and an external test queue, and the method further includes:

[0027] Inputting the internal test queue into each of the basic learners to obtain five sets of internal prediction values, and taking the average value of the internal prediction values ​​as the internal test set;

[0028] Input the external test queue into each of the basic learners to obtain five sets of external prediction values, and use the average value of the external prediction values ​​as the external test set;

[0029] The obesity genetic risk prediction result is output based on the external test set input and the distributed gradient enhancement library model.

[0030] According to one embodiment of the present application, obtaining the model evaluation result of the obesity genetic risk model includes:

[0031] Evaluating a model evaluation result of the obesity genetic risk model according to a model evaluation method;

[0032] The model evaluation method includes calculating the accuracy, recall, F1 score and area under the ROC curve of the obesity genetic risk model.

[0033] In a second aspect, an embodiment of the present application provides an electronic device, comprising: a processor; a memory for storing processor executable instructions; wherein the processor is configured to implement the method for constructing an obesity genetic risk model based on a stacking model as described in the first aspect when executing the instructions.

[0034] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, wherein the instructions instruct a device to execute the method for constructing an obesity genetic risk model based on a stacking model as described in the first aspect.

[0035] The method for constructing a genetic risk model for obesity based on a stacking model, an electronic device, and a storage medium provided in the embodiments of the present application integrate the basic learner and perform stacking training in combination with meta-classifier logistic regression, effectively integrating the advantages of different algorithms and improving the accuracy and stability of obesity genetic risk prediction. The single nucleotide polymorphism data is screened multiple times, and the intersection is taken to obtain a significant genetic feature set, ensuring that the screened features are strongly correlated with the genetic risk of obesity, further improving the feature extraction efficiency of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic flow chart of a method for constructing an obesity genetic risk model based on a stacking model provided in one embodiment of the present application.

[0037] Figure 2A , Figure 2B , Figure 2C , Figure 2D and Figure 2E A schematic diagram of the mean square error of the obesity genetic risk model provided in the embodiments of the present application.

[0038] Figure 3 A schematic diagram of an obesity genetic risk model provided in one embodiment of the present application.

[0039] Figure 4A and Figure 4B A schematic diagram of predicting the cumulative risk of death using the obesity genetic risk model provided in one embodiment of the present application.

[0040] Figure 5 A schematic diagram of an electronic device provided in one embodiment of the present application.

[0041] Main component symbols

[0042] Electronic devices 20

[0043] Processor 21

[0044] Memory 22

[0045] Method steps S100-S600 DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0047] It should be noted that, in the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0048] It should be noted that, in the embodiments of the present application, words such as "first" and "second" are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order. Features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way.

[0049] Based on the implementations in this application, all other implementations obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] Obesity is an important public health issue worldwide and has been shown to be closely related to a variety of chronic diseases (such as cardiovascular disease, diabetes, metabolic syndrome, etc.). In recent years, with the development of genomics technology, the genetic basis of obesity has gradually attracted attention. Studies have shown that an individual's susceptibility to obesity is affected by genetic polymorphisms, which usually exist in the form of single nucleotide polymorphisms (SNPs). Although there have been a large number of gene-related obesity studies, how to use this genetic information to accurately assess an individual's risk of obesity remains a major challenge in current scientific research and clinical practice.

[0051] Currently, there is a lack of reliable tools in the relevant technology for assessing the genetic risk of obesity and the risk of death related to obesity.

[0052] In view of this, the embodiment of the present application provides a method for constructing a genetic risk model for obesity based on a stacking model, an electronic device and a storage medium. By integrating a variety of basic learners such as random forests, distributed gradient boosting library models, adaptive boosting and logistic regression, and combining meta-classifier logistic regression for stacking training, the advantages of different algorithms are effectively integrated, and the accuracy and stability of obesity genetic risk prediction are improved. The single nucleotide polymorphism data are multiplexed by random forests and distributed gradient boosting library models, and the intersection is taken to obtain a significant genetic feature set to ensure that the selected features are highly correlated with the genetic risk of obesity, further improving the feature extraction efficiency of the model. Five cross-validations are introduced during the training process of the first layer of basic learners to avoid overfitting problems, and to improve the generalization ability of the model on different data sets, thereby improving the stability and reliability of the model. Through the multi-layer training of the stacking model, the prediction results of the basic learners provide more abundant training data for the second layer XGBoost model, and the model performance is more robust after hierarchical optimization, enhancing its ability to predict the genetic risk of obesity. The model is evaluated through indicators such as accuracy, recall, F1 score, and area under the ROC curve, and the model parameters are optimized based on the evaluation results to ensure that the model can maintain good performance in practical applications.

[0053] In conjunction with the accompanying drawings, some embodiments of the application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0054] Figure 1 1 is a schematic diagram of a method flow chart provided by an embodiment of the present application. Figure 1 The method for constructing an obesity genetic risk model based on a stacking model shown includes at least the following steps: S100: obtaining a data set, the data set including genetic characteristic data, the genetic characteristic data including single nucleotide polymorphisms;

[0055] S200: Apply random forest to screen single nucleotide polymorphisms to obtain the first genetic feature set related to the genetic risk of obesity; S300: Apply distributed gradient boosting library model to screen single nucleotide polymorphisms to obtain the second genetic feature set related to the genetic risk of obesity; S400: Obtain the significant genetic feature set based on the first genetic feature set and the second genetic feature set; S500: Construct an obesity genetic risk model; S600: Obtain the model evaluation results of the obesity genetic risk model, and optimize the parameters of the obesity genetic risk model based on the model evaluation results.

[0056] S100: Acquire a data set, the data set including genetic characteristic data, and the genetic characteristic data including single nucleotide polymorphism.

[0057] In an embodiment of the present application, the method for constructing an obesity genetic risk model based on a stacking model includes acquiring a data set in step S100, where the data set includes genetic characteristic data, and the genetic characteristic data includes single nucleotide polymorphisms.

[0058] It is understandable that in the process of implementing this application, obtaining a data set is a key basic step. The data set not only contains physical examination data, but also includes genetic characteristic data, especially single nucleotide polymorphism (SNP) information related to obesity. These SNPs are the core features of studying the genetic risk of obesity and can reflect the potential relationship between gene variation and obesity. By collecting comprehensive and accurate physical examination data and SNP data, the accuracy of subsequent screening steps and the reliability of model prediction results can be ensured.

[0059] In the present application embodiment, data is first obtained from the data sets of the UK Biobank and the Southern Hospital. Specifically, the data is cleaned and samples with missing BMI values ​​and genetic data are removed to ensure the integrity and reliability of the data. After data cleaning, a training cohort, an internal test cohort, and an external test cohort are obtained. The training cohort is used for model training and contains most of the data. The internal test cohort is used for internal validation of the model to evaluate the performance of the model on the training data. The external test cohort is used for independent evaluation of the model to ensure the generalization ability of the model. Through this step, the high quality of the subsequent model training data is ensured to avoid the impact of noise and missing data on the model results.

[0060] S200: Application of random forests to screen single nucleotide polymorphisms to obtain the first set of genetic signatures associated with genetic risk of obesity.

[0061] In an embodiment of the present application, the method for constructing an obesity genetic risk model based on a stacking model includes, in step S200, applying a random forest to screen single nucleotide polymorphisms to obtain a first genetic feature set related to obesity genetic risk.

[0062] It is understandable that random forest is an integrated learning method that analyzes the importance of features by constructing multiple decision trees. In this embodiment, random forest is used to screen SNPs associated with obesity. The principle is to evaluate the contribution of each SNP to obesity risk prediction by calculating the importance of the split nodes in different trees. The first genetic feature set screened includes those SNPs with higher importance scores in obesity risk prediction. The purpose of this step is to reduce the interference of noise data and improve model performance.

[0063] S300: Application of a distributed gradient boosting library model to screen single nucleotide polymorphisms to obtain a second set of genetic features associated with genetic risk of obesity.

[0064] In an embodiment of the present application, the method for constructing an obesity genetic risk model based on a stacking model includes, in step S300, applying a distributed gradient boosting library model to screen single nucleotide polymorphisms to obtain a second genetic feature set related to obesity genetic risk.

[0065] It is understandable that the distributed gradient boosting library model (such as XGBoost) is a commonly used boosting method, which is used to further screen SNPs in this embodiment. The model trains weak learners through iterative weighting, gradually reducing errors, thereby improving the effects of classification and regression. XGBoost evaluates the contribution of each SNP and screens out the second genetic feature set to ensure that these features can better reflect the risk of obesity. Compared to random forests, XGBoost can capture more complex feature relationships, so the combination of the two can fully extract SNP information.

[0066] S400: Obtain a significant genetic feature set based on the first genetic feature set and the second genetic feature set.

[0067] In an embodiment of the present application, the method for constructing an obesity genetic risk model based on a stacking model includes, in step S400, obtaining a significant genetic feature set based on the first genetic feature set and the second genetic feature set.

[0068] In an embodiment of the present application, obtaining a significant genetic feature set according to the first genetic feature set and the second genetic feature set includes: taking the intersection of the first genetic feature set and the second genetic feature set to obtain the significant genetic feature set.

[0069] It can be understood that the significant genetic feature set is calculated by the intersection of the first and second genetic feature sets. This method ensures that the features that are finally retained are not only important in the random forest, but also have strong relevance in the XGBoost model, thereby improving the accuracy of feature screening. This method can effectively reduce redundant features and focus on extracting genetic features that are highly correlated with obesity.

[0070] In an embodiment of the present application, obtaining a significant genetic feature set based on the first genetic feature set and the second genetic feature set also includes: analyzing the first genetic feature set and the second genetic feature set according to lasso regression to obtain the significant genetic feature set.

[0071] It is understood that Lasso regression (LASSO) is a regression method with L1 regularization, which is used to further screen features. In this embodiment, Lasso regression introduces a penalty term to force irrelevant feature coefficients to tend to zero, thereby reducing the complexity and overfitting problems of the model. Through Lasso regression analysis, researchers can further optimize the set of significant genetic features to ensure that the selected features have a significant contribution to obesity risk in the actual model.

[0072] In this example, random forest and XGBoost were used to preliminarily screen the single nucleotide polymorphisms recorded in the sample to screen out candidate genetic features associated with obesity risk. Subsequently, LASSO regression was used to further refine these preliminary screening results and select the SNP features that are most significant for obesity risk. LASSO regression can avoid overfitting problems and retain the features most relevant to obesity risk by applying L1 regularization.

[0073] Please also read FIG. 2A to FIG. 2E , FIG. 2A to FIG. 2E It is a schematic diagram of the mean square error of the obesity genetic risk model provided in the application example.

[0074] In the present application embodiment, the embodiment of the present invention provides the effect display of the LASSO model screening different numbers of single nucleotide polymorphisms (SNPs) in the training set. Specifically, the figure reveals the mean square error (MSE) corresponding to the model as the number of SNPs changes. In order to determine the optimal number of SNPs, the principle of minimum mean square error is adopted, that is, the number of SNPs that minimizes the model prediction error is selected. The λ value is the regularization parameter of the LASSO model, which is used to control the complexity of the model, and the coefficient size of the model is constrained by introducing the L1 regularization term.

[0075] According to the diagram, as the λ value changes, the mean square error of the model shows a trend of first decreasing and then stabilizing. Finally, at the optimal λ value, i.e. 1378, a dotted line is drawn, indicating that the corresponding number of SNPs at this time can allow the model to achieve the minimum mean square error, indicating that the model at this time has the best predictive ability. This application aims to reduce the complexity of the model and prevent overfitting by adjusting the regularization parameter λ of the LASSO model, while screening out the SNPs that are most relevant to the genetic risk of obesity.

[0076] It is understandable that the embodiments of the present application can obtain significant genetic feature sets in two different ways. The first method is to take the intersection of the feature sets screened by the random forest and distributed gradient boosting library models to obtain those features that are highly important in both models. The second method is to perform further analysis through lasso regression, which uses L1 regularization to reduce the number of features and zero redundant or unimportant feature coefficients to ensure that the final screened significant genetic feature set has the best predictive effect on obesity risk in the actual model.

[0077] S500: Constructing a genetic risk model for obesity.

[0078] In an embodiment of the present application, the method for constructing an obesity genetic risk model based on a stacking model includes constructing an obesity genetic risk model in step S500. The obesity genetic risk model is a stacking model, and the obesity genetic risk model includes a basic learner and a meta-classifier. The basic learners include: random forest, distributed gradient boosting library model, adaptive boosting and logistic regression. Among them, the algorithms in the basic learners predict obesity genetic risk according to the significant genetic feature set, and output the prediction results to the meta-classifier respectively, and the meta-classifier is used to output the obesity genetic risk prediction results

[0079] It is understandable that the obesity genetic risk model is constructed by stacking models. It is understandable that the stacking model is an integrated learning method that combines multiple different basic learners, namely random forest, distributed gradient boosting library model, adaptive boosting, and logistic regression, to enhance the predictive ability of the model. Each basic learner predicts obesity risk based on a set of significant genetic features and passes its output results to a meta-classifier. The meta-classifier, lasso logistic regression, generates the final obesity genetic risk prediction by integrating these prediction results. This model architecture helps to combine the advantages of multiple learners, reduce the limitations of a single model, and improve the accuracy and robustness of the prediction.

[0080] Please also read Figure 3 , Figure 3 This is a schematic diagram of an obesity genetic risk model provided in one embodiment of the present application.

[0081] like Figure 3 As shown in the figure, the input of the obesity genetic risk model is genetic feature data, which is input into the meta-classifier distributed gradient boosting library after passing through the basic learner random forest, distributed gradient boosting library model, adaptive boosting, and logistic regression algorithm, and finally outputs the obesity-related polygenic risk score (OPRS). The value range of OPRS is 0 to 1, with 0 indicating the lowest genetic risk of obesity and 1 indicating the highest genetic risk of obesity.

[0082] In the present application embodiment, the OPRS score uses a quartile method, with the low-risk group being the 1st quartile, the medium-risk group being the 2nd to 3rd quartile, and the high-risk group being the 4th quartile. The 1st quartile is the low-risk group, and these participants have the lowest genetic risk of obesity. The 2nd to 3rd quartile is the medium-risk group, and these participants have a moderate genetic risk. The 4th quartile is the high-risk group, and these participants have the highest genetic risk of obesity.

[0083] Please also read Figure 4A and Figure 4B , Figure 4A and Figure 4B This is a schematic diagram of predicting the cumulative risk of death using an obesity genetic risk model provided in one embodiment of the present application.

[0084] like Figure 4A and Figure 4B As shown in the figure, at 5 years, the cumulative incidence of death in the high-risk group was higher than that in the low-risk group and the medium-risk group, both for the internal training cohort and the external training cohort, and there was no significant difference between the low-risk group and the medium-risk group. At this time, the difference in the risk of death between the low-risk group and the medium-risk group was not obvious, indicating that the mortality rates of the low-risk and medium-risk groups were relatively close in the early stage.

[0085] At 10 years, the cumulative incidence of death in the high-risk group was still significantly higher than that in the low-risk and medium-risk groups, and the cumulative incidence of death in the medium-risk group was slightly higher than that in the low-risk group. Although the cumulative mortality rate in the high-risk group was still significantly higher than that in the other two groups, the mortality rate in the medium-risk group began to exceed that in the low-risk group, showing a more obvious differentiation trend. This stage showed that the difference in the cumulative mortality rates between the medium-risk group and the low-risk group gradually emerged, and the risk of death in the high-risk group continued to increase.

[0086] At 13 years, the death risks of the three groups of people further differentiated, forming a clear mortality rate hierarchy. There was a clear differentiation between the low-risk group, the medium-risk group and the high-risk group. The cumulative mortality rate of the high-risk group was much higher than that of the medium- and low-risk groups, showing a significant high-risk feature. The cumulative mortality rate of the medium-risk group was higher than that of the low-risk group, showing a trend of increasing long-term mortality in the medium-risk group. The low-risk group had the lowest cumulative mortality rate, indicating that it had a better survival expectation.

[0087] Understandably, these results validate the effectiveness of the obesity genetic risk model in long-term prediction, especially its ability to identify and classify high-risk groups, which can provide a scientific basis for early intervention and risk management. At the same time, the model can not only distinguish high-risk from low-risk individuals, but also provide effective predictions for medium-risk groups, which contributes to more accurate individualized medical management strategies.

[0088] It is understandable that the obesity genetic risk model provided by the embodiment of the present application combines multiple machine learning algorithms to improve prediction accuracy through a hierarchical structure. The basic learner processes the genetic characteristic data respectively, and the meta-classifier is responsible for integrating the prediction results and finally outputting a comprehensive score (OPRS). The quartile division method of OPRS provides a clear obesity risk classification for individuals, which helps to guide individuals to manage health risks.

[0089] S600: Obtaining a model evaluation result of the obesity genetic risk model, and optimizing parameters of the obesity genetic risk model according to the model evaluation result.

[0090] In an embodiment of the present application, the method for constructing an obesity genetic risk model based on a stacking model includes, in step S600, obtaining a model evaluation result of the obesity genetic risk model, and optimizing the parameters of the obesity genetic risk model according to the model evaluation result.

[0091] It is understandable that obtaining the evaluation results of the obesity genetic risk model is a key step in optimizing the model. It is understandable that model evaluation mainly quantifies the performance of the model by calculating common evaluation indicators such as accuracy, recall, F1 score and area under the ROC curve (AUC). These indicators can comprehensively evaluate the classification effect of the model, especially for complex problems such as obesity genetic risk that are affected by multiple factors. According to the evaluation results, the parameters of the model can be fine-tuned to ensure that the model performs more stably on different data sets, avoid overfitting or underfitting problems, and further improve the generalization ability and prediction effect of the model.

[0092] Specifically, obtaining a model evaluation result of the obesity genetic risk model includes evaluating the model evaluation result of the obesity genetic risk model according to a model evaluation method. The model evaluation method includes calculating the accuracy, recall rate, F1 score and area under the ROC curve of the obesity genetic risk model.

[0093] It is understandable that, in the present application embodiment, obtaining the model evaluation result of the obesity genetic risk model includes measuring the performance of the model according to a series of evaluation indicators. Specifically, these evaluation methods include accuracy (used to measure the overall accuracy of the prediction results), recall (used to measure the sensitivity of the model in detecting obesity risk), F1 score (comprehensively considering the balance of accuracy and recall), and the area under the ROC curve (AUC, used to evaluate the discrimination ability of the model under different thresholds). Through these evaluation indicators, the performance of the obesity genetic risk model in different scenarios can be fully understood, and the model can be optimized if necessary.

[0094] In the embodiment of the present application, the meta-classifier is a logistic regression model, and the meta-classifier generates a prediction value of obesity genetic risk by integrating the prediction results of the basic learner.

[0095] It is understandable that the meta-classifier uses a logistic regression model, which generates the final genetic risk prediction value of obesity by integrating the prediction results of all basic learners. The core function of the meta-classifier is to weightedly integrate the output results of the basic learners to compensate for the deviations that may be generated by a single model, thereby improving the overall prediction performance of the model. Since the logistic regression model can process the features of linear combinations, it is a commonly used meta-classifier in ensemble learning, which helps to improve the interpretability and robustness of the model.

[0096] In an embodiment of the present application, the first genetic feature set obtains the importance score of the genetic feature through a random forest algorithm, and the genetic features with importance scores higher than a preset threshold are screened as the first genetic feature set. The second genetic feature set obtains the importance score of the genetic feature through a distributed gradient enhancement library model, and the genetic features with importance scores higher than a preset threshold are screened as the second genetic feature set.

[0097] It can be understood that the first genetic feature set and the second genetic feature set are screened by the random forest algorithm and the distributed gradient boosting library model, respectively. The random forest algorithm calculates the importance score of each genetic feature and screens out features with scores higher than a preset threshold to form the first genetic feature set; similarly, the distributed gradient boosting library model also screens according to the importance score of the genetic feature, and features with high scores will be included in the second genetic feature set. Through the combined screening of these two methods, it can be ensured that the key features that are highly correlated with obesity risk are retained in the genetic feature set.

[0098] In an embodiment of the present application, the method further comprises randomly dividing the significant genetic feature set into five subsets with equal data amounts. The four subsets are sequentially applied to train the basic learner, and one subset is applied to verify the obesity genetic risk model to form a cross-validation. After completing five cross-validations, five sets of predicted values ​​are obtained, and a training set is formed based on the five sets of predicted values.

[0099] It is understandable that five cross validation techniques are applied in the present application embodiment to ensure the generalization ability and robustness of the model. Specifically, the significant genetic feature set can be randomly divided into five subsets with equal data amounts. In each iteration, four subsets are used as training sets for training basic learners, and the remaining subset is the most validation set for verifying the performance of the model. By five cross validations, the overfitting problem of the model can be effectively prevented, while improving its performance consistency on different data sets. The five groups of predicted values ​​finally obtained will be combined into a new training set to further improve the robustness of the model.

[0100] It can be understood that in each iteration, the base learner is trained using the training set and generates predictions on the validation set. After completing five iterations, the five sets of predictions are aggregated to form a new training set for the base learner, which is used as the input of the meta-classifier. In this way, the stability and generalization ability of the model are enhanced through cross-validation, ensuring consistency of performance on different data sets.

[0101] In an embodiment of the present application, the data set includes an internal test cohort and an external test cohort, and the method further includes inputting the internal test cohort into each basic learner to obtain five sets of internal prediction values, and taking the average value of the internal prediction values ​​as the internal test set. The external test cohort is input into each basic learner to obtain five sets of external prediction values, and taking the average value of the external prediction values ​​as the external test set. The obesity genetic risk prediction result is output according to the external test set input and the distributed gradient enhancement library model.

[0102] Understandably, the performance of the obesity genetic risk model is also evaluated through internal and external test cohorts. Each base learner generates five sets of prediction values ​​for the internal and external test cohorts, respectively, and takes their average as the new internal or external test set. The average value of the external test set is used as input, combined with the output of the distributed gradient boosting library model, to finally generate the prediction results of obesity genetic risk. This approach not only verifies the effect of the model on internal data, but also verifies the generalization ability of the model through the external test set to ensure its robustness under different data distributions.

[0103] In the present application embodiment, the method for constructing a genetic risk model for obesity based on a stacking model has good scalability and adaptability. Specifically, with the continuous accumulation of new data, the training queue and the test queue are regularly updated to ensure that the model is always trained and optimized based on the latest data. Through the incremental learning method, the model parameters are gradually adjusted to adapt to the changing trend of the data. In addition to single nucleotide polymorphisms (SNPs), other genetic characteristics (such as gene expression levels, metabolite data) and environmental factors (such as eating habits, exercise) are combined for feature fusion. Through the comprehensive analysis of multidimensional features, the prediction accuracy of the model for obesity risk is improved. According to the differences in genetic characteristics of different populations (such as different races and age groups), the parameter settings of the stacking model are optimized. Develop a sub-model specifically for a specific population to ensure the applicability and accuracy of the model in different populations. Develop a real-time monitoring system to continuously track the health data of individuals and dynamically update their OPRS scores. Through a real-time feedback mechanism, timely adjust the health management strategy to improve the effectiveness of intervention measures. Through the above-mentioned expansion and optimization measures, the stacking model can better adapt to different application scenarios and population needs, and continuously improve the accuracy and practicality of obesity genetic risk prediction.

[0104] The method for constructing a genetic risk model for obesity based on a stacking model provided in the embodiment of the present application improves the accuracy and stability of genetic risk prediction for obesity by integrating multiple machine learning algorithms and combining meta-classifier logistic regression for stacking training. Through multiple feature screening and cross-validation, the model is ensured to have good generalization ability and robustness. At the same time, the interpretability and transparency design of the model enhances the user's trust in the model prediction results and provides a scientific basis for individualized health management and public health decision-making.

[0105] Figure 5 This is an electronic device 20 provided in an embodiment of the present application. Figure 5 As shown, the electronic device 20 includes at least the following parts: a processor 21 and a memory 22 .

[0106] In the embodiment of the present application, the memory 22 is used to store instructions executable by the processor 21, and the processor 21 is configured to implement the following when executing the instructions: Figure 1 The method for constructing an obesity genetic risk model based on a stacking model is shown.

[0107] In an embodiment of the present application, a computer-readable storage medium includes instructions, the instructions instructing a device to execute the following Figure 1 The method for constructing a genetic risk model for obesity based on a stacking model is described in the embodiment of the present invention. For example, the instruction instructs the device to execute the following Figure 1 The method for constructing an obesity genetic risk model based on a stacking model is shown in steps S100 to S600.

[0108] The program operating in the electronic device 20 involved in one embodiment of the present application may be a program (a program that enables a computer to function) that controls a central processing unit (CPU) and the like to realize the functions of the above-mentioned embodiment involved in one scheme of the present invention. Then, the information processed by these devices is temporarily stored in a random access memory (RAM) when processing, and then stored in various ROMs such as a read-only memory (Flash ROM) and a hard disk drive (HDD), and is read, modified, and written by the CPU as needed.

[0109] It should be noted that part of the electronic device 20 of the above embodiment may be implemented by a computer. In this case, a program for implementing the control function may be recorded in a computer-readable recording medium and implemented by reading the program recorded in the recording medium into a computer system and executing it.

[0110] It should be noted that the "computer system" mentioned here refers to a computer system built into the electronic device 20, and adopts a computer system including hardware such as an OS and peripheral devices. In addition, "computer-readable recording medium" refers to removable media such as a floppy disk, a magneto-optical disk, a ROM, a CD-ROM, and a storage device such as a hard disk built into the computer system.

[0111] Furthermore, "computer-readable recording media" may include: media that dynamically store programs for a short period of time, such as communication lines when sending programs via networks such as the Internet or communication lines such as telephone lines; and media that store programs for a fixed period of time, such as volatile memories inside computer systems that serve as servers or clients in this case. In addition, the above-mentioned program may be a program for realizing a part of the above-mentioned functions, or a program that can realize the above-mentioned functions by combining with a program already recorded in a computer system.

[0112] In addition, the electronic device 20 in the above-mentioned embodiment can also be implemented as a collection (device group) composed of multiple devices. Each device constituting the device group can have a part or all of the functions or functional blocks of the electronic device 20 in the above-mentioned embodiment. As a device group, it is sufficient to have all the functions or functional blocks of the electronic device 20.

[0113] It is understandable that the method for constructing a genetic risk model for obesity based on a stacking model, an electronic device and a storage medium provided in the embodiment of the present application, by integrating a variety of basic learners such as random forest, distributed gradient boosting library model, adaptive boosting and logistic regression, and stacking training is performed in combination with meta-classifier logistic regression, which effectively integrates the advantages of different algorithms and improves the accuracy and stability of obesity genetic risk prediction. Single nucleotide polymorphism data are multiplexed by random forest and distributed gradient boosting library models, and the intersection is taken to obtain a significant genetic feature set to ensure that the features screened out are highly correlated with the genetic risk of obesity, further improving the feature extraction efficiency of the model. Five cross validations are introduced during the training process of the first layer of basic learners to avoid overfitting problems, and to improve the generalization ability of the model on different data sets, thereby improving the stability and reliability of the model. Through the multi-layer training of the stacking model, the prediction results of the basic learners provide richer training data for the second layer XGBoost model, and the model performance is more robust after hierarchical optimization, which enhances its ability to predict the genetic risk of obesity. The model is evaluated through indicators such as accuracy, recall, F1 score, and area under the ROC curve, and the model parameters are optimized based on the evaluation results to ensure that the model can maintain good performance in practical applications.

[0114] Those skilled in the art should recognize that the above embodiments are only used to illustrate the present application and are not intended to be limiting of the present application. As long as they are within the spirit and scope of the present application, appropriate changes and modifications to the above embodiments are within the scope of protection claimed in the present application.

Claims

1. A method for constructing an obesity genetic risk model based on a stacking model, characterized in that: The method comprises: Acquiring a data set, the data set comprising genetic characteristic data, the genetic characteristic data comprising single nucleotide polymorphisms; Random forests were applied to screen single nucleotide polymorphisms to obtain the first set of genetic features associated with genetic risk of obesity; A distributed gradient boosting library model was applied to screen single nucleotide polymorphisms to obtain a second genetic signature set associated with genetic risk of obesity; Obtaining a significant genetic feature set based on the first genetic feature set and the second genetic feature set; Constructing an obesity genetic risk model, wherein the obesity genetic risk model is a stacking model, and the obesity genetic risk model includes a basic learner and a meta-classifier; The basic learners include: random forest, distributed gradient boosting library model, adaptive boosting and logistic regression; Wherein, the algorithms in the basic learner predict the genetic risk of obesity according to the significant genetic feature set, and output the prediction results to the meta-classifier, respectively, and the meta-classifier is used to output the genetic risk prediction results of obesity; A model evaluation result of the obesity genetic risk model is obtained, and parameters of the obesity genetic risk model are optimized according to the model evaluation result.

2. The method for constructing an obesity genetic risk model based on a stacking model according to claim 1, characterized in that: The obtaining of a significant genetic feature set according to the first genetic feature set and the second genetic feature set comprises: The first genetic feature set and the second genetic feature set are intersected to obtain the significant genetic feature set.

3. The method for constructing an obesity genetic risk model based on a stacking model according to claim 1, characterized in that: The obtaining of a significant genetic feature set according to the first genetic feature set and the second genetic feature set also includes: The first genetic feature set and the second genetic feature set are analyzed according to lasso regression to obtain the significant genetic feature set.

4. The method for constructing an obesity genetic risk model based on a stacking model according to claim 1, characterized in that: The meta-classifier is a logistic regression model, and the meta-classifier generates a prediction value of obesity genetic risk by integrating the prediction results of the basic learner.

5. The method for constructing an obesity genetic risk model based on a stacking model according to claim 1, characterized in that: The first genetic feature set obtains the importance score of the genetic feature by a random forest algorithm, and the genetic features with the importance score higher than a preset threshold are selected as the first genetic feature set; The second genetic feature set obtains the importance score of the genetic feature through a distributed gradient enhancement library model, and the genetic features with importance scores higher than a preset threshold are screened as the second genetic feature set.

6. The method for constructing an obesity genetic risk model based on a stacking model according to claim 1, characterized in that: The method further comprises: Randomly dividing the significant genetic feature set into five subsets with equal data amounts; Applying the four subsets in sequence to train the basic learner, and applying one subset to validate the obesity genetic risk model to form a cross-validation; After completing five cross-validations, five sets of prediction values ​​are obtained; A training set is formed based on the five sets of predicted values.

7. The method for constructing an obesity genetic risk model based on a stacking model according to claim 1, characterized in that: The data set includes an internal test queue and an external test queue, and the method further includes: Inputting the internal test queue into each of the basic learners to obtain five sets of internal prediction values, and taking the average value of the internal prediction values ​​as the internal test set; Input the external test queue into each of the basic learners to obtain five sets of external prediction values, and use the average value of the external prediction values ​​as the external test set; The obesity genetic risk prediction result is output based on the external test set input and the distributed gradient enhancement library model.

8. The method for constructing an obesity genetic risk model based on a stacking model according to claim 1, characterized in that: The step of obtaining the model evaluation result of the obesity genetic risk model comprises: Evaluating a model evaluation result of the obesity genetic risk model according to a model evaluation method; The model evaluation method includes calculating the accuracy, recall, F1 score and area under the R OC curve of the obesity genetic risk model.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the method for constructing an obesity genetic risk model based on a stacking model as described in any one of claims 1 to 8 when executing the instructions.

10. A computer-readable storage medium, characterized in that: It includes instructions that instruct the device to execute the method for constructing an obesity genetic risk model based on a stacking model as described in any one of claims 1 to 8.

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