223 results about "Lasso regression" patented technology

The invention relates to a data processing method and system for multi-source complex biological information data. According to the method, expression profile data, gene variation data and clinical survival data are collected, and unified standardization processing is carried out on the collected data. Feature alignment is performed on different source data based on sample identifiers, a joint feature expression matrix is constructed, and a context dependency relationship across data types is maintained. On the basis, multi-stage feature screening is carried out through Lasso regression and information gain evaluation in sequence, and an optimal feature subset used for modeling is obtained. And further training a risk scoring model by adopting a Cox proportional risk regression method, and calculating a risk scoring value of the sample by utilizing the constructed continuous scoring function. And finally, dividing the score value into a plurality of risk levels, and generating a survival curve of each level in combination with a Kaplan-Meier estimation method so as to verify the risk layering effect and prediction significance of the model. According to the method, the accuracy of biological information modeling can be improved, and the method has good universality and practical value.

The invention relates to an advanced gastric cancer survival prediction system based on Lasso regression, Cox regression and an interpretable machine learning technology, and belongs to the technical field of medical artificial intelligence and intelligent decision support. According to the system, by collecting multi-modal clinical data (including demographic information, TNM staging, treatment modes, tumor grading and the like) of a patient, survival-related variables are screened by adopting Lasso regression and a Cox proportional risk model, and an optimized feature set is constructed. Based on the feature set, the system integrates various mainstream machine learning algorithms (such as XGBoost, Random Forest, SVM, Logistic regression and the like) to construct a prediction model, compares the performance of each model, and selects a model with an optimal effect as a main model. And hyper-parameter tuning is performed on the model through grid search and cross validation, so that the precision and generalization ability of the model are improved. An SHAP interpretability analysis method is introduced into the system, transparent interpretation is carried out on a model output result from the global level and the individual level, and the importance and directional effect of all variables in survival prediction are determined. Finally, the model is deployed on a terminal device, a doctor is supported to automatically output the survival probability and an explanation result after inputting patient information, and a reference basis is provided for clinical treatment decision and personalized management. The system has the advantages of high prediction precision, high interpretability, convenience in use, sustainable optimization and the like, is suitable for clinical aid decision-making scenes, and has good application prospects and popularization values.

The invention discloses a prostate cancer three-classification risk layering method based on an integrated learning model, and relates to the technical field of data processing and analysis. The method comprises the following steps: collecting clinical information and pathological data of a patient with increased PSA, and dividing the data into a training set and a test set; the training set is preprocessed, and prediction features are screened through LASSO regression; constructing a plurality of machine learning base models based on the features, and training and optimizing through cross validation; soft voting is constructed through an integration strategy, and an integration model is stacked; setting double thresholds according to the integrated model prediction probability, and establishing a layering rule; combining an integrated model and rules to form a three-classification model, and judging low, high and medium risks according to probabilities; and finally verifying the model diagnosis performance in the test set. According to the method, through cross-modal feature integration and ensemble learning, the method is PSAlt; accurate risk stratification is provided for 30 ng / mL people, biopsy decision-making efficiency is optimized, and excessive puncture and missed diagnosis risks are reduced.

The invention discloses a prone position ARDS patient death risk prediction system based on interpretable machine learning, which constructs an interpretable machine learning prediction model based on clinical data of an ARDS patient before the ARDS patient receives prone position ventilation treatment, and is used for identifying high-death-risk crowds in an ICU (Intensive Care Unit). According to the method, an optimal prediction model is determined by performing preprocessing and correlation and collinearity screening on original variables, selecting key features by adopting Lasso regression and combining multi-model training and cross validation. On the basis, an SHAP method is introduced to explain a model decision basis, a column graph is constructed, visual expression of a risk assessment result is realized, and clinical understandability and practicability of the model are improved, so that auxiliary support is provided for precise treatment and resource allocation.

The invention discloses a data-based XGBoost model construction method for evaluating the cerebral hemorrhage recurrence risk of a patient, and the method comprises the steps: S10, reading and processing the data of the cerebral hemorrhage patient, and obtaining the data of a training set; s20, receiving the training set data, processing the training set data by adopting LASSO regression and a step-by-step recursion program, and outputting a plurality of variable features; s30, judging whether the p value of each characteristic variable is less than 0.05 or not; s40, when the p value of the characteristic variable is smaller than 0.05, outputting the variable characteristic; s50, constructing and optimizing parameters of the first model, and outputting an XGBoost model; and S60, carrying out visual interpretation on an evaluation result of the XGBoost model by using an SHAP (Secure Hardware Application Program) and an LIME (Local Instrumentation Model Element) interpreter Compared with the prior art, the method provided by the invention has the advantages that the model resolution AUC is 0.92 and the prediction accuracy is 0.95 by taking nine characteristic factors as internal verification of the model; the model resolution ratio AUC tested by the test set is 0.93, and the prediction accuracy is 0.91.

The invention relates to the technical field of data fusion, in particular to a multi-source heterogeneous data fusion processing system and method based on artificial intelligence deep learning, and the system comprises a data collection module, a data preprocessing module, a feature extraction module, a data fusion module, a result evaluation module and an application output module. The data collection module collects original data of different data sources and transmits the original data to the data preprocessing module, the data preprocessing module carries out space-time self-supervision training and data enhancement on the original data and then outputs preprocessed data, and the feature extraction module carries out feature extraction on the preprocessed data and outputs feature data. The data fusion module carries out fusion processing on the feature data to obtain fusion data, the result evaluation module evaluates the fusion data, the application output module outputs the evaluated fusion data, a Lasso regression model is adopted to carry out preliminary feature screening, and the accuracy and efficiency of feature extraction are improved.

The invention belongs to the technical field of rolling, and relates to a method for setting roll bending force and roll shifting amount for controlling the shape of a hot-rolled strip steel, which comprises the following steps of: acquiring historical production data of strip steel in actual production; the method comprises the following steps: performing abnormal value detection on strip steel historical production data by using a 3 sigma principle, removing abnormal values, and dividing the data into a training set and a test set; establishing a convexity prediction model based on Lasso regression, and training the convexity prediction model by using the training set data; testing the trained convexity prediction model based on Lasso regression by using the test set; establishing a plate shape objective function; constraint conditions of rolling variables are established according to all rack equipment in the finishing mill group and the plate shape theory; and a snake vulture optimization algorithm is adopted, the roll bending force and the roll shifting amount of each rack in the finishing mill group are searched within the constraint condition range, the plate shape target function is made to be minimum, and the roll bending force set value and the roll shifting amount set value are obtained.

The invention discloses an intelligent scheduling management system and method for a power grid communication maintenance plan, and belongs to the field of intelligent scheduling management, and the method comprises the steps: collecting the historical operation data of power grid communication, and carrying out the preprocessing, and obtaining a sample data set; performing feature screening on the sample data set based on an LASSO regression model to obtain key features; constructing a prediction model by using the screened key features, and training and evaluating the prediction model by using the divided training sample set and verification sample set to obtain a fault prediction model; inputting the real-time operation data into the fault prediction model, obtaining the prediction probability of the equipment fault, and comparing the prediction probability with a preset probability threshold to judge whether the equipment has a fault or not; the method comprises the following steps: establishing a maintenance plan scheduling optimization model by setting an optimization target and a constraint condition, and solving by adopting a heuristic algorithm to generate an optimal maintenance plan scheduling scheme; and outputting the optimal maintenance plan scheduling scheme to the system, and executing the optimal maintenance plan scheduling scheme after the optimal maintenance plan scheduling scheme is audited and adjusted by a manager.

The invention discloses a system energy-saving structure optimization control method based on a dynamic balance strategy, and particularly relates to the technical field of energy management systems, which comprises the steps of dynamic model construction, LASSO regression decoupling, transient compensation, multi-machine system coordination control, power distribution network parameter optimization, multi-target control model construction and improved sparrow algorithm. And source-load-storage coordinated control of the new energy microgrid is realized, and short-term and long-term electric quantity balance is optimized. According to the system energy-saving structure optimization control method based on the dynamic balance strategy, through the optimization control method, the adaptive capacity of the system to rapid changes is enhanced, stable operation of a power grid under various changes is ensured, the stability and economical efficiency of the system are improved, a user is effectively guided to carry out energy adjustment, and the user experience is improved. The energy utilization efficiency is improved, the global optimization capability of the system is enhanced, the efficient and stable operation of the micro-grid is realized, the energy consumption is reduced, and the energy-saving optimization is realized.

The invention provides a gestational diabetes risk prediction system and method based on multi-modal data fusion, and the system comprises a data collection module which is used for integrating clinical indexes and medical record text data; the data preprocessing module converts the multimode data into numerical values and text variables which can be used for modeling; the variable screening module is used for extracting data features by adopting LASSO regression in combination with a recursive feature elimination algorithm and a Clinical-BERT model; the data prediction module is used for constructing a GDM risk prediction model through a dual-channel calculation unit and a fusion unit, generating an accurate risk probability and providing an interpretable clinical index in combination with an SHAP value; and a prediction result is output through the output module in the forms of a dynamic column diagram, a webpage calculator and an API interface, so that clinical operation and application are facilitated. According to the method, the limitation of a traditional prediction method is broken through, the prediction precision and the real-time monitoring capability are improved, and the development of precise medical treatment is promoted.

The invention discloses a COPD-FE risk prediction method based on disease and symptom combination, and is applied to the technical field of chronic obstructive pulmonary disease risk prediction. Comprising the following steps: acquiring chronic obstructive pulmonary frequent acute exacerbation influence factor data of a patient; the influence factors are screened through LASSO regression and an improved Boruta algorithm respectively; the LASSO independent influence factors and the Boruta independent influence factors are combined in different modes, a Logistic regression model and an XGBoost model are trained, and a plurality of COPD-FE risk prediction models are obtained; and evaluating the performance of all the COPD-FE risk prediction models, and selecting the COPD-FE risk prediction model meeting the requirement to predict the COPD-FE risk. According to the method, clinical data of patients are collected, a risk prediction model is constructed in combination with a feature selection method and machine learning, and an optimal model is screened out through comprehensive evaluation.

The invention discloses a hepatocellular carcinoma postoperative early recurrence prediction method based on multi-modal fusion. The method comprises the following steps: firstly, integrating clinical data of a training set, a preoperative enhanced CT image and a postoperative full-view digital pathological image, and carrying out standardized correction; then, traditional image omics features and deep learning features are extracted from the CT image, cell nucleus morphological features and tumor microenvironment spatial configuration features are extracted from the pathological image, and key feature signatures are screened out through a maximum correlation minimum redundancy algorithm (mRMR) and LASSO regression in combination with clinical features. And then carrying out progressive model construction by adopting an XGBoost algorithm, sequentially establishing a clinical single-mode model, an image single-mode model, a pathological single-mode model and a multi-mode fusion model, and explaining and visualizing the models by utilizing an SHAP value and a Grad-CAM technology. Finally, the performance of the model is evaluated in a multi-dimensional mode through internal cross validation, foresight and external independent validation, risk layering is carried out based on the prediction probability, and individualized postoperative management is guided.

The invention discloses a colorectal cancer postoperative venous thromboembolism risk prediction method and system, and the method comprises the steps: obtaining clinical pathology data of a historical patient before the patient receives a total proctectomy, carrying out the preprocessing of the clinical pathology data, employing a standardized score to detect an abnormal value in the clinical pathology data, carrying out the filling of a missing value, and carrying out the prediction of the risk of the venous thromboembolism. Performing standardization processing on the continuous variables; based on the preprocessed clinical pathology data, extracting clinical pathology features by adopting Lasso regression and grid search; constructing a Stacking integration model based on a random forest, a CatBoost model and logistic regression, and training the Stacking integration model by using the extracted clinical pathological features; and by using the trained Stacking integrated model, processing clinical pathology data of a target patient before the target patient receives the total proctectomy to obtain a prediction result. The colorectal cancer postoperative venous thromboembolism risk can be effectively predicted, and support is provided for clinical decision making.

The invention discloses a diagnosis method for predicting tuberculosis risk by using blood routine indexes, which comprises the following steps: (1) collecting open-source blood routine data, preprocessing, and dividing into a training set, a test set and a verification set; (2) screening blood routine examination and 25 indexes derived from the blood routine examination, and obtaining a preliminary screening result through LASSO regression analysis; (3) inputting the preliminary screening result into seven machine learning models, including a logistic regression model, a random forest model, a naive Bayes model, a K proximity model, a support vector machine model, an XGBoost model and a GBM model, for analysis to obtain a final variable combination and an optimal model; (4) inputting the verification set into the optimal model to obtain a DCA decision curve and a calibration curve; early discovery and precise diagnosis and treatment of tuberculosis are promoted, and meanwhile the pressure of medical resources is effectively relieved.

The invention provides a muscle fatty degeneration risk assessment method based on machine learning, and relates to the technical field of disease risk assessment. The method comprises the following steps: acquiring a data sample of an HD patient; carrying out feature screening on related features in the data sample according to a Boruta algorithm and Lasso regression based on interaction importance among the features, and generating an important feature set; based on the important feature set, constructing and training a plurality of machine learning prediction models by using the corresponding data samples, and determining an optimal model; inputting data of an actual HD patient into the optimal model to generate a prediction result; performing global explanation and local explanation on the prediction result based on the SHAP value; and generating a risk assessment result according to the global explanation and the local explanation of the prediction result. By adopting the method, the features can be accurately screened, and the prediction result of the model can be explained by accurately utilizing the SHAP value.

The invention discloses a method for predicting the curative effect of brain stimulation on MCI based on a multi-modal image, and belongs to the technical field of medical data process.The method comprises the steps that through a multi-modal MRI feature fusion model, structure and functional MRI indexes are synchronously integrated, feature selection and dimension reduction are conducted through comparison between independent sample t test groups and LASSO regression, and the curative effect of brain stimulation on MCI is predicted; compared with the existing method, the method has the advantages that the obvious contribution of the change of the collaborative structure image index and the functional image index to the predicted curative effect is verified through the structure-function coupling analysis, and the prediction model is constructed, so that the prediction efficiency is improved. The contribution direction and strength of each image feature to curative effect prediction are analyzed and quantified in combination with SHAP values, feature weights are automatically optimized according to SHAP value distribution, and model high-contribution feature brain regions are revealed through SHAP so as to clarify curative effect biomarkers.

The invention relates to the technical field of medical data analysis and clinical risk prediction, provides a construction method of a glucocorticoid induced diabetes risk prediction model based on an LASSO algorithm, and belongs to the technical scheme of data processing executed on computing equipment. Clinical data of inpatients receiving systemic glucocorticoid treatment are collected, key variables are screened through LASSO regression, and a Logistic regression model is constructed to achieve risk prediction. The model is composed of four conventional clinical indexes, has good distinction degree and calibration degree, and finally realizes individualized risk assessment through column diagram form output. The method is simple, practical and accurate, and is suitable for SDM prediction and intervention management of clinical high-risk groups.

The invention relates to the field of bioinformatics, in particular to a genome selection method, device and equipment, a medium and a computer program product. Integrating a plurality of machine learning models, including 13 algorithms such as a support vector machine (SVM), linear regression, Ridge regression, Lasso regression and the like; by combining automatic hyper-parameter optimization, parallel computing and efficient data preprocessing technologies, the breeding prediction precision and the computing efficiency are remarkably improved. The device comprises an IASML software module and an IASML cloud platform module, wherein software supports command line operation and is suitable for large-scale data calculation; the cloud platform provides a graphical interface and supports online data uploading, model selection, real-time monitoring and result downloading. According to the method, the problems of singleness, limited data scale, poor interactivity and the like of an existing tool model are solved, and an efficient, flexible and reproducible intelligent solution is provided for animal and plant breeding and genetic research.

The invention discloses a breast cancer risk prediction method based on machine learning and multi-dimensional data, and belongs to the technical field of medical health information.The method comprises the steps that based on an NHANES database, diet, living habits and other information are collected, and a data set is formed; performing pretreatment; screening meaningful data features by using three feature selection methods of LASSO regression, mRMR and forward selection, and obtaining a final feature data set after intersection; dividing a training set and a test set; establishing a risk prediction model by using an SVM machine learning method, and learning the training set; performing model performance analysis on the test set to obtain a risk prediction probability of a final training model; the method has the advantages of multi-source data integration, high-precision prediction, personalized evaluation, dynamic updating and the like, risk factors of the breast cancer can be effectively mined, theoretical support is provided for prevention and treatment of the breast cancer, high-risk group screening is guided, morbidity reduction is assisted, early diagnosis and early treatment are achieved, and development of female health undertaking is promoted.

The invention discloses a power distribution network reactive voltage index evaluation method under different operation conditions, and relates to the field of voltage index evaluation, and the method comprises the following steps: S1, carrying out the division according to different operation states of a power distribution network, and formulating the corresponding operation conditions; s2, calculating six indexes including voltage deviation, a voltage fluctuation coefficient, a three-phase imbalance coefficient, a harmonic component ratio, frequency deviation and a power factor PF to construct a data set, and establishing and using a sparse encoder and a lasso regression model to determine a calculation mode of a voltage quality comprehensive index value; and S3, aiming at the operation condition of the power distribution network, considering the voltage quality comprehensive index value, and integrating four indexes including the line loss rate, the short circuit ratio and the voltage qualification rate to evaluate the reactive voltage of the power distribution network under the condition. According to the method, the operation health conditions of the power distribution network under various working conditions can be quickly identified, and a scientific basis is provided for optimization and adjustment; and an operation strategy of the power distribution network is guided in actual operation, and stable and efficient operation of the system is ensured.

The embodiment of the invention provides a myocardial infarction risk prediction model training method and related equipment, and belongs to the technical field of machine learning. The myocardial infarction risk prediction model training method comprises the steps of obtaining initial training data, performing multiple times of characteristic variable screening on the initial training data through a single factor analysis method and an LASSO regression method to obtain training case data, and inputting the training case data into a BP neural network model for nonlinear data processing to obtain the prediction probability of myocardial infarction occurrence. And performing parameter optimization on the BP neural network model according to the prediction probability and the real occurrence probability to obtain a myocardial infarction risk prediction model. According to the embodiment of the invention, nonlinear processing can be carried out on high-dimensional or multivariable training case data through the BP neural network model, and the processing capability of complex data and the accuracy and stability of myocardial infarction risk prediction are improved.

The invention relates to a training method of a respiratory tract infection disease progress and prognosis prediction model. The training method comprises the following steps: extracting mRNA from peripheral blood of a target patient, and carrying out transcriptome sequencing to obtain a sequencing result; based on the ferroptosis related gene set, comparing ferroptosis score differences of two groups of patients with community-acquired pneumonia and sepsis, and screening corresponding ferroptosis related genes with statistical significance from a sequencing result; screening out genes meeting preset conditions from the ferroptosis related genes based on LASSO regression; and establishing an RTI clinical outcome prediction model through logistic regression by taking whether the patient is sepsis or not as an outcome dichotomy variable and taking the screened gene expression quantity as a prediction variable. According to the invention, after the prediction model is subjected to machine learning screening such as LASSO and the like, the core feature with the highest prediction value is reserved, so that the risk of over-fitting of the model on training data is reduced.

The invention discloses a power failure information monitoring method and system based on a real-time position, and relates to the technical field of power failure information monitoring, and the method comprises the steps: collecting the data of an intelligent electric meter, calculating the power flow of a line, carrying out the sparse regression through an LASSO algorithm, and recognizing a key line which is strongly related to a power failure event; window sliding processing is carried out based on the key line, and an event matrix is generated according to statistical characteristics; the TF-IDF value of each feature is calculated based on the time frequency of the statistical features in the event matrix. According to the method, power flow data and voltage phase angle data are used as feature matrixes, the sparsity of LASSO regression is beneficial to eliminating non-critical line data with large noise or interference, the complexity and redundancy of a model are reduced, feature importance of a critical line in different time windows is enhanced through a TF-IDF weighting matrix, and the accuracy of the LASSO regression is improved. According to the method, the SVM model is helped to recognize the power failure event more accurately, and after SVM preliminary classification, isolated misjudgment can be corrected through MDC auxiliary recognition and clustering analysis.

The invention discloses a carotid artery pulse wave velocity prediction method and device based on machine learning and a medium, and the method comprises the steps: carrying out the time domain feature analysis and correlation analysis of an original pulse wave data set of each test sample, and obtaining a plurality of key time domain features of each carotid artery position of each test sample; obtaining a total regression coefficient of the key time domain features of each carotid artery position by adopting an LASSO regression model; then, performing permutation and combination on the carotid artery positions to obtain a plurality of key time domain features under each carotid artery position combination of each test sample; constructing a test sample feature set of each carotid artery position combination, training a machine learning model according to the test sample feature set, and optimizing to obtain a carotid artery pulse wave velocity prediction model and a target carotid artery position combination; and performing carotid artery pulse wave velocity prediction on a to-be-predicted sample by using the carotid artery pulse wave velocity prediction model to obtain a predicted pulse wave velocity. The detection efficiency and the detection accuracy of the carotid artery pulse wave velocity are improved.

The invention belongs to the technical field of crucible deformation resistance prediction, and relates to a quartz crucible high-temperature deformation resistance rate prediction method based on machine learning. According to the method, key historical data are collected, and information such as aluminum alloy components, crucible physical attributes, using conditions and the deformation resistance rate is covered; through data preprocessing, the quality and consistency of a data set are ensured; through statistical methods such as Pearson's correlation coefficients, LASSO regression, principal component analysis and the like, features having significant influence on the anti-deformation rate are screened out, feature engineering is carried out to construct new interactive features, and the features can reflect the complex relation between the smelting conditions and the physical attributes of the crucible; the deep learning model is introduced, so that the prediction model can learn a deep mode in the data, and the prediction precision is remarkably improved; finally, the trained model can accurately predict the high-temperature deformation resistance rate of the quartz crucible according to real-time data, a scientific basis is provided for production decision making, and optimization of the production process and reduction of the cost are facilitated.

The invention provides a photoresist process parameter compensation method and a compensation control system, and the method comprises the steps: building a Lasso regression model through collected process parameters in a plurality of batches of photoresist processes, and carrying out the data training, the method comprises the following steps: reversely analyzing a nonlinear influence mechanism of an external environment temperature on the photoresist thickness through a model, then obtaining a compensation rule of a real-time environment temperature through a Lasso regression model, and dynamically adjusting a photoresist spin-coating rotating speed in a current batch photoresist process so as to maintain the thickness of the current batch photoresist to be constant; according to the method, the stability of the critical dimension (CD) of the photoetching process is improved, a photoetching process window is expanded (the exposure dose fluctuation range of a photoetching machine is widened and the debugging cost of the photoetching process is reduced), accurate temperature compensation of the photoetching process is realized, so that the no-load energy consumption of the photoetching machine is reduced, and the product yield and the reliability are improved.

The invention relates to the technical field of biological medicine, in particular to construction and application of a candida patient death risk prediction model. The model is based on traditional SOFA scoring, core variables are screened through single-factor and multi-factor logistic analysis and LASSO regression analysis, respiratory system, blood coagulation function and circulatory system SOFA scoring and key indexes such as the maximum lactic acid value, the minimum albumin value and the maximum blood urea nitrogen (BUN) are integrated, and a prediction model is constructed through logistic regression. Through multi-center data verification, the AUC of an internal verification queue reaches 0.826, the AUC of an external verification queue is 0.813, and a calibration curve shows that the predicted death rate is highly consistent with the actual death rate. Patients can be divided into a high risk group and a low risk group through a 13-score threshold value, and the 28-day survival rate difference is significant (plt; 0.05) of the method. An efficient and convenient early risk assessment tool is provided for critical candida patients, and clinical precise intervention and prognosis improvement are assisted.

The invention provides an underground water source water quality early warning method and system for environmental safety, and relates to the technical field of environmental protection and water quality safety, and the method comprises the steps: obtaining a plurality of water quality indexes of an underground water source in different historical periods and index values corresponding to the water quality indexes; screening the water quality indexes by combining a Lasso regression model and an FISTA algorithm, and determining key water quality indexes; constructing a two-dimensional tensor according to the key water quality index and the index value; constructing a water body pollution prediction model based on CNN and LSTM in combination with an attention mechanism; inputting the two-dimensional tensor into a water body pollution prediction model, and determining a pollution factor concentration; calculating the deviation between the pollution factor concentration and the pollution factor threshold value, and determining the water quality risk grade; and performing early warning on the water quality of the underground water source by combining an early warning grading strategy according to the water quality risk grade. The water quality safety of the underground water source can be ensured, and potential threats to the environment and human health are reduced.

The invention provides a method for constructing an ankylosing spondylitis ossification progress prediction model based on metabonomics and artificial intelligence. The method comprises the following steps: carrying out metabonomics analysis and peak extraction to obtain a characteristic ion peak table, carrying out compound identification on characteristic ion peaks, and carrying out characteristic screening through a lasso regression method, a multivariate statistical method of unbiased variable selection and a BORUTA method to obtain candidate characteristics related to the ankylosing spondylitis ossification progress; carrying out data standardization processing, principal component analysis and orthogonal partial least-partial-square discriminant analysis on the characteristic ion peak table, and carrying out pathway enrichment analysis to obtain differential metabolites; an LR model, an RF model and an SVM model used for distinguishing the ossification progress speed of the AS patient are established based on known risk factors, candidate features and differential metabolites, the efficiency of the three models is verified and compared, and finally the ankylosing spondylitis ossification progress prediction models with the optimal efficiency are obtained and serve as the LR model and the RF model.

The invention discloses a lung adenocarcinoma prognosis prediction model based on heme metabolism related genes and a construction method and application thereof. According to the invention, a heme metabolism related gene set is screened through a molecular characteristic database, and a gene significantly related to prognosis is screened by using transcriptome and clinical data of lung adenocarcinoma in a TCGA database and combining single-factor Cox regression, LASSO regression and multivariable Cox analysis. And through multi-time iterative modeling, selecting high-frequency stable genes and corresponding regression coefficient mean values to construct a heme metabolism risk score. According to the method, a random survival forest model is adopted to carry out iteration evaluation on gene importance for multiple times, the first 50% of genes which have the maximum influence on the model are screened, and finally the five important characteristic genes related to the heme metabolism risk are identified by taking an intersection with an LASSO result. The application provides new theoretical basis and technical support for precise treatment of lung adenocarcinoma, and has important clinical application value.