199 results about "Logistische regression" patented technology

The invention relates to the technical field of health monitoring, in particular to a cardiovascular disease data fusion and multi-dimensional health assessment method, which comprises the following steps of: acquiring multi-source data of a patient through a sensor and wearable equipment, preprocessing the acquired data, ensuring data integrity, extracting physiological features by adopting a convolutional neural network, and evaluating the health of the patient. The method comprises the steps of extracting non-physiological data features through principal component analysis, constructing a multi-dimensional feature set, identifying cardiovascular disease related risk factors through a random forest algorithm, distributing weights through a logistic regression algorithm to generate individualized risk scores, analyzing time sequence trends of the risk factors based on a long and short-term memory network model, and generating dynamic risk scores in real time. When the score exceeds a preset threshold value, an early warning mechanism is automatically triggered, and a patient and medical staff are notified through an application program; according to the method, accurate assessment and personalized intervention of cardiovascular health are realized through multi-source data fusion and dynamic analysis, and the method has high efficiency and timeliness.

The invention discloses a cardiovascular disease risk prediction method and system based on dietary multi-modal data and ensemble learning, and the method comprises the steps: constructing a multi-modal set through integrating multi-source heterogeneous data such as demographic statistics, dietary nutrition, clinical physiological and biochemical indexes and lifestyles; data cleaning is completed based on a box plot method and missing value processing, and key features are screened through Pearson's correlation coefficients, variance expansion factors and feature importance evaluation; a plurality of heterogeneous base models are fused by adopting a Stacking integration framework, a meta-feature matrix is generated through five-fold layered cross validation, and multi-level decision fusion is realized through a logistic regression meta-model; and quantifying the contribution weight of the dietary characteristics to the risk in combination with an SHAP method, and generating a visual interpretation chart and personalized intervention suggestions. According to the method, the accuracy and the stability of a prediction result are remarkably improved, the contribution degree and the action mechanism of dietary factors and other characteristics to the prediction result can be deeply analyzed, powerful support is provided for accurate prevention and personalized treatment of cardiovascular diseases, and the method has good practical value.

Provided in the present invention are a method for constructing a pre-eclampsia risk assessment prediction model, and a prediction product, a prediction system, and a related application of the method. Specifically, the method of the present invention comprises: constructing a logistic regression analysis model on the basis of acquired clinical indexes of a pregnant woman, and obtaining the prior probability of early-onset pre-eclampsia or late-onset pre-eclampsia in the pregnant woman; performing median-of-multiples calibration on an acquired MAP, IGFBP1 and / or PLGF of the pregnant woman, and respectively constructing a multivariate normal distribution model for a sick pregnant woman and a multivariate normal distribution model for a healthy pregnant woman on the basis of an MAP MoM value and an IGFBP1 MoM value and / or a PLGF MoM value, so as to obtain the conditional probability of early-onset pre-eclampsia or late-onset pre-eclampsia in the pregnant woman; and in view of the prior probability and the conditional probability, calculating the posterior probability of early-onset pre-eclampsia or late-onset pre-eclampsia in the pregnant woman on the basis of a Bayesian model. In the present invention, the detection of related protein markers and the obtaining of maternal clinical information data are relatively easy, the accuracy of predicting early-onset pre-eclampsia and late-onset pre-eclampsia is relatively high, and clinical popularization and application are facilitated; therefore, the present invention is of great value to the prevention and early diagnosis of a disease.

The invention relates to a composition for identifying oral squamous cell carcinoma based on saliva sample detection and application, and belongs to the technical field of molecular biology. The composition provided by the invention is used for identifying the oral squamous cell carcinoma by detecting the expression of SNORD52 and / or SNORD65 in a saliva sample; the composition comprises a specific reverse transcription stem-loop primer, a specific detection forward primer, a specific detection probe and a universal reverse primer. According to the composition disclosed by the invention, the expression of SNORD52 and / or SNORD65 in a saliva sample is detected, and a binomial logistic regression model is combined to perform auxiliary judgment on the oral squamous cell carcinoma, so that the composition has relatively high sensitivity and specificity, and the identification accuracy of the oral squamous cell carcinoma can be improved. The invention further provides a kit containing the composition, a detection object of the kit is a saliva sample, and the kit has the advantages of being small in sample dosage, noninvasive in sampling, convenient, rapid, small in risk and the like.

The invention discloses a classification method and a classification device for pancreatic neuroendocrine tumors. The classification method comprises the following steps: using a pre-trained segmentation model to segment a plurality of HE full slice images from a plurality of patients; quantizing each segmented HE full slice image to determine a plurality of first feature parameters; based on the cellular morphology and topological features of each HE full-slice image, extracting a plurality of second feature parameters from each HE full-slice image; for each HE full slice image, screening out a target feature from the plurality of first feature parameters and the plurality of second feature parameters, and calculating a pathological omics score; inputting the pathological omics scores and the conventional pathological features of the plurality of HE full slice images into a logistic regression model for training to obtain a trained logistic regression model; and classifying the HE full slice images to be classified by using the logistic regression model. According to the method, pancreatic neuroendocrine tumor G classification can be quickly and accurately realized at low cost.

The invention relates to the field of noninvasive prenatal diagnosis, and particularly discloses a preeclampsia noninvasive screening method based on deep sequencing 8bp oligonucleotide double-fragment characteristics, which comprises the following steps: collecting preeclampsia and healthy pregnant woman peripheral blood samples, and extracting free DNA for high-throughput sequencing; the method comprises the following steps: extracting core 8-mer sequences' GTGCGCCC 'and' GATGGGGT 'in a long fragment of 150-200bp through bioinformatics analysis; an integrated support vector machine, K-nearest neighbor, extreme gradient lifting, a random forest and a multi-layer perceptron are combined with a logistic regression element classifier to construct a stacking model, the frequency of a core sequence is normalized, machine learning analysis is carried out, and the preeclampsia risk is predicted. According to the invention, two 8bp oligonucleotide characteristic fragments are specifically screened, and a deep learning architecture of multi-model fusion is combined, so that the limitations of low specificity and invasive detection of a traditional screening method are effectively broken through.

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 discloses a pelvic floor assessment method and system combining pelvic floor myoelectricity and muscle force signals, and relates to the technical field of pelvic floor muscle assessment, and the method comprises the steps: synchronously collecting pelvic floor myoelectricity signals and pressure signals, and recording data of a pre-resting stage, a rapid contraction stage, a tension contraction stage, an endurance contraction stage and a post-resting stage; the pelvic floor electromyographic signals and the pressure signals are preprocessed, and statistical characteristics are extracted based on the preprocessed signals; constructing a pelvic floor evaluation model, wherein the pelvic floor evaluation model comprises a logistic regression model, a long-short term memory network based on a cross attention mechanism and a multi-layer perceptron classifier; and training the pelvic floor evaluation model by using an Adam optimizer and a cross entropy loss function, and optimizing parameters of the pelvic floor evaluation model through evaluation indexes. According to the pelvic floor evaluation model, the timing sequence features and the statistical features are combined, more comprehensive signal interpretation is achieved, the generalization ability of the pelvic floor evaluation model is improved, and the pelvic floor health condition can be recognized more accurately.

The invention provides a diabetes chronic complication early warning method and device, a medium and computer equipment. The early warning method comprises the following steps: acquiring body data of a diabetic patient; screening the body data by adopting an LASSO logistic regression model to generate a data training set; training the multi-layer perception model by using the data training set to obtain a diabetic complication early warning model; obtaining to-be-detected body data, inputting the body data into the diabetic complication early warning model for prediction after screening, and generating an early warning result; by the adoption of the method, dimension reduction is conducted on the collected body data through the LASSO logistic regression model, the training data of the model can be effectively reduced, the computing power requirement for the training and reasoning process is lowered, the model can be conveniently deployed to a local computing end, and therefore the diabetic patient can conveniently achieve home detection. Compared with conventional blood detection, the non-invasive blood detection device has the advantages of being non-invasive and small in operation difficulty.

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 relates to the technical field of medical data processing, and particularly provides a brain tumor distinguishing model construction method, which is characterized in that a radiomics model is constructed based on a brain-tumor junction region (BTI), and the model can effectively distinguish glioblastoma (GBM) and isolated brain metastases (SBM); the GBM and the SBM have significant overlap in subjective image features, the brain tumor distinguishing model based on radiomics constructed by the invention effectively makes up for uncertainty possibly caused by subjective judgment, and the BTI region-based logic regression model provided by the invention has high prediction capability and clinical practicability. An important auxiliary tool is provided for clinical diagnosis, and the diagnosis accuracy is remarkably improved.

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.

The embodiment of the invention provides a continuous heart rate monitoring method. The continuous heart rate monitoring method comprises the steps that an initial PPG signal and an initial ACC signal are obtained; performing preprocessing to obtain a PPG frequency spectrum and an ACC frequency spectrum; analyzing the PPG spectrum and the ACC spectrum based on a logistic regression model spectrum, judging whether a same-frequency condition exists or not, and if so, performing same-frequency processing on the PPG spectrum to obtain an adjusted PPG spectrum; judging whether the heart rate value at the previous moment exists or not; if not, executing an initial value selection algorithm based on the adjusted PPG spectrum to obtain a current heart rate value; if yes, determining the current heart rate value based on a frequency tracking algorithm. According to the scheme, the ACC and PPG signals are fused, whether the signals have the same frequency or not is judged through machine learning, and if the signals have the same frequency, the PPG frequency spectrum is processed, so that the influence of motion noise is effectively inhibited. An initial value selection algorithm is designed, the initial value of the real heart rate can still be accurately positioned when the signal quality is poor through amplitude accumulation and trend analysis, a reliable starting point is provided for follow-up tracking, and the accuracy and stability of continuous heart rate monitoring 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 method for monitoring parameters of a patient during surgery with extracorporeal circulation serves to estimate the presence of AKI risk and comprises calculating a dynamic global index of AKI risk as a logistic regression deriving from a static AKI risk index and a dynamic index of AKI risk. The value and / or the time trend of such global index of AKI risk can be advantageously displayed on a monitor during the surgery. The dynamic AKI risk index is calculated at least on the basis of the extracorporeal circulation time, the minimum level of oxygen supply and the exposure time to oxygen supply below a critical threshold which are measured or determined repeatedly during surgery. Preferably, the dynamic AKI risk index is also calculated on the basis of the minimum mean arterial pressure during surgery and / or the maximum concentration of lactate in the blood during surgery and / or the minimum hematocrit during surgery and / or the fact that the patient has been subjected to transfusion(s) during the extracorporeal circulation.

The invention discloses a disease early screening method based on a Stacking integrated model. Comprising the following steps: firstly, carrying out abnormal value processing by adopting a box graph theory, secondly, carrying out missing value processing by adopting a layered mean value filling method, carrying out correlation analysis and carrying out data cleaning to obtain a preprocessed data set, then, selecting several types of better base learners by applying an AUC index value, and finally, integrating the selected base learners by using a Stacking strategy, so as to obtain a data set; a new feature training set and a new test set are obtained through K-fold cross validation, and logistic regression is selected as a meta-learner for disease prediction. According to the method provided by the invention, the method is applied to experimental analysis of a pima Indiana diabetes data set, and the related evaluation indexes of the Stacking integrated model and the intermediate machine learning model are compared, so that the obtained Stacking integrated model has more excellent classification prediction performance, and can provide auxiliary decision information support for early disease screening.

The invention relates to a disease information management method and system based on multi-source data, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: forming multi-source heterogeneous data through employing the obtained infectious disease data and chronic disease data, and carrying out the preprocessing with the corresponding data features as a reference, on one hand, risk prediction and clustering analysis are performed on infectious disease related data through a logistic regression model to obtain risk assessment information, and a propagation trend is predicted through the model; on the other hand, chronic disease data are predicted and evaluated through the model, a medical advice result is obtained, and finally disease control information management is conducted in combination with all the data. Therefore, a set of perfect disease prevention and control information management is realized, on one hand, more timely and accurate infectious disease detection and early warning are realized, and the efficiency and effect of major infectious disease prevention and control work are improved, and on the other hand, chronic disease management is perfected, and the problems of diagnosis and treatment errors and lagging of patient treatment consciousness caused by incomplete data are avoided.

The invention discloses an early-stage lung cancer prediction method based on a multi-modal eccDNA marker, and relates to the technical field of liquid biopsy, and the method comprises the following steps: collecting a peripheral blood sample, carrying out differential centrifugal separation on plasma, constructing a cfDNA library, and carrying out double-end sequencing to obtain original sequencing data; extracting structural features through an eccDNA model, and inputting the structural features into a first machine learning model to generate a circular DNA score; extracting variation features through an SNV model, and inputting the variation features into a second machine learning model to generate a spectrum feature score; extracting copy number features through a CNV model, and inputting the copy number features into a third machine learning model to generate copy number scores; carrying out probability distribution calibration on the circular DNA score, the spectrum feature score and the copy number score; inputting the three types of molecular features into a deep neural network to generate a first fusion score; inputting the calibrated score into a logistic regression model to generate a second fusion score; and generating a final lung cancer risk probability according to the first fusion score and the second fusion score.

The invention relates to the technical field of diabetes prediction, in particular to a risk assessment method and system for diabetic complications, and the method comprises the following steps: obtaining CGM data, and carrying out the preprocessing of the CGM data; dividing the preprocessed CGM data into the CGM data in different time periods; performing feature extraction on the CGM data of each time period; taking the extracted features as input of a machine learning algorithm, and adopting the machine learning algorithm for training; analyzing the influence of the extracted features on diabetic complications to obtain important features; a logistic regression model is adopted, and the corresponding relation between changes of the important features in different time periods and changes of the diabetic complication risk is calculated; according to the corresponding relation, the blood glucose value in the time period corresponding to the features is precisely regulated and controlled. According to the method, the CGM data in one day are divided into different time periods, the influence of the change of the CGM data in each time period on the diabetic complications is analyzed, and support is provided for precise management of the diabetic complications.

The application discloses an evaluation method for predicting the safety of EGFR TKIs monotherapy for NSCLC. The method comprises the following steps: obtaining the data of a plurality of patients receiving EGFR TKIs treatment; taking MDRAE (maximum grade of drug-related adverse event) as a safety index, constructing a safety evaluation final model, and the final model is an ordered logistic regression model, the covariates of the final model include normalized steady-state trough concentration, EGFR TKIs treatment history, gender, baseline glutamyltransferase, baseline uric acid, baseline creatine kinase, baseline platelets and baseline lymphocytes; obtaining the data of a target patient, inputting the numerical values of each covariate in the data of the target patient into the final model; determining the first probability of MDRAE of the target patient being grade 3 and above, the second probability of MDRAE being grade 2 and the third probability of MDRAE being grade 1 and below according to the final model, and taking the grade corresponding to the maximum probability in the three probabilities as the predicted grade of MDRAE of the target patient. The application improves the accuracy of predicting the safety of EGFR TKIs monotherapy for NSCLC.

The invention relates to a novel marker for a glioblastoma tumor transition region and application of the novel marker, and belongs to the technical field of biological detection. In order to solve the technical problems that in the prior art, tumor invasion leading edge markers cannot be effectively recognized, and postoperative residues and prognosis are difficult to accurately evaluate, 19 gene markers with high specificity expression in a tumor transition region, such as SELENOP, SHTN1 and CTNNA3, are screened out by combining space transcriptome analysis with a machine learning algorithm. The expression level of the marker is detected through digital imaging, nucleic acid sequencing and other technologies, a logistic regression prediction model with the AUC value larger than 0.95 is constructed, and the logistic regression prediction model is applied to diagnostic kit preparation, tumor layering screening and prognosis evaluation. According to the invention, precise recognition of the tumor transition region is realized, and a molecular basis is provided for postoperative recurrence monitoring, individualized treatment scheme making and novel treatment target screening.

The invention relates to the technical field of medical imaging, in particular to a breast cancer tissue HER2 automatic scoring method and device based on a characteristic curve, and the method comprises the steps: carrying out the reading operation of a full-slice image of immunohistochemical staining; extracting a target region of interest of the full slice image; slicing and screening the target region of interest to obtain a plurality of image slices; calculating a percentage-saturation characteristic curve of each image slice, and extracting characteristic information of the percentage-saturation characteristic curve; based on the feature information of the percentage-saturation feature curve, performing preliminary HER2 scoring on each image slice in combination with a rule base and a logistic regression classification algorithm; and integrating the scoring results of the image slices, and outputting a final HER2 score and a characteristic curve visualization graph. According to the method, the feature curve is combined with the rule base and the logistic regression classification algorithm, so that the objectivity of HER2 scoring is improved, and the problems of subjectivity and low efficiency of manual evaluation are reduced.

The invention provides a chest tumor patient postoperative pulmonary infection prediction method and system based on deep learning, a logic regression model established based on patient data is used as a risk factor for pulmonary infection judgment, and the risk factor is further explained and evaluated by establishing a hybrid expert model. By comprehensively reflecting indexes of the overall evaluation performance, a verification result shows that the accuracy of the hybrid expert model is better than that of a single model, the hybrid expert model is not influenced by distribution of discrete feature and continuous feature data in a training set and a test set, and the hybrid expert model has good generalization ability.

The invention discloses a neonatal congenital heart disease screening method based on machine learning, and the method comprises the following steps: 1, designing a questionnaire according to literatures related to adverse birth result risk factors, collecting infant cardiac ultrasound examination data, and then preprocessing the data; step 2, randomly dividing the data obtained in the step 1 into a training set and a test set, and performing dimension reduction processing on features by using lASSO regression; and step 3, based on the features obtained in the step 2, training a prediction model by using machine learning algorithms such as a random forest, a support vector machine, a lightweight gradient elevator, logistic regression and extreme gradient lifting. According to the method, the model is optimized through combination of multi-step data processing and various machine learning algorithms, related features can be comprehensively and scientifically screened, the accuracy of the prediction model is improved, a more reliable and effective method is provided for screening the neonatal congenital heart disease, early-stage accurate screening is facilitated, and the screening efficiency and quality are improved.

The invention provides a neurosurgery craniocerebral postoperative drainage monitoring method and system, and relates to the technical field of medical instruments, and the method comprises the steps: collecting intracranial pressure data, body position change data, drainage bag height data and liquid flow rate data; the intracranial pressure data, the drainage bag height data and the liquid flow rate data are preprocessed based on a local outlier factor and a Z-score standardization algorithm; performing coupling analysis on the preprocessed intracranial pressure data and the preprocessed liquid flow rate data based on a machine learning algorithm to obtain a dynamic drainage dynamic index; performing dynamic correction analysis on the dynamic drainage dynamic index and the preprocessed drainage bag height data based on a logistic regression algorithm to obtain a height correction drainage efficiency index, and further adjusting the height of the drainage bag; based on the neural network model, constructing a drainage bag automatic adjustment model, and then obtaining a height automatic adjustment command; automatically adjusting the height of the drainage bag according to the automatic height adjusting command; the nursing workload is greatly reduced.

The invention discloses an ADHD auxiliary diagnosis model construction method, control equipment and a program product, relates to the field of medical artificial intelligence, and obtains a high-performance ADHD auxiliary diagnosis model through a small sample. The method comprises the following steps: preprocessing acquired sample data, screening out a predetermined number of indexes with the importance higher than that of a diagnosis result in the sample data to obtain a sample set, and dividing the sample set to obtain a training set and a test set; and constructing a logistic regression classifier model, training the logistic regression classifier model by using the training set, and testing the trained logistic regression classifier model by using the test set to obtain the ADHD auxiliary diagnosis model. According to the method, the problem that an ADHD prediction model depends on the sample size is solved, and the feasibility of clinical application is improved.

The invention discloses a system and a method for analyzing and predicting malnutrition of patients with elderly mild cognitive impairment, belongs to the technical field of malnutrition analysis and prediction, incorporates a more comprehensive and systematic index system, and covers potential risk factors, traditional risk factors and laboratory data of the patients with elderly mild cognitive impairment. The comprehensive breakthrough of the research perspective is realized, and the limitation defect of the prior art is broken through. The decision tree model and the logistic regression model are comprehensively compared, a precise malnutrition risk prediction model for the elderly MCI patient is constructed, a reference with practical value is provided for clinical prevention and treatment work, and optimization and perfection of a malnutrition prevention and treatment strategy of the elderly mild cognitive impairment patient are expected to be promoted. According to the method, the defects that in the prior art, analysis of the malnutrition of the elderly mild cognitive impairment patient has limitation, and a traditional prediction model is insufficient in decision making and pertinence aspects of the malnutrition of the elderly mild cognitive impairment patient are effectively overcome.

The invention provides a privacy protection calculation method based on group learning and cardiovascular disease prediction, and the method comprises the steps: obtaining local medical data, carrying out the preprocessing, and dividing the data into a training set and a test set; training at least one base learner according to the local data characteristics and outputting a prediction probability; a Gaussian difference privacy mechanism is adopted to disturb prediction output; dynamically adjusting the privacy budget epsilon in an exponential decay mode according to the number of rounds in the training process; receiving prediction results after disturbance of all the clients and aggregating the prediction results by using a logistic regression model as a meta-learner; training a lightweight student model through knowledge distillation; the prediction capability is evaluated according to multiple performance indexes, and the influence degree of the differential privacy mechanism on the model performance is verified through a model test result under multiple epsilon values; and constructing an attack model to evaluate the protection effect of the privacy mechanism. On the premise of ensuring data privacy security, the cardiovascular disease prediction capability is improved, and the optimal balance between privacy protection and model performance is realized.

The invention relates to the technical field of bioinformatics and medical data processing, and discloses a polygene molecular diagnosis model and a construction method and application thereof.The construction method comprises the steps that a data acquisition module constructs a genome, clinical phenotype and environmental exposure data matrix, a preprocessing module executes regression filling and standardizes continuous variables, and a data processing module performs data processing; the feature screening module executes double-layer screening by using LASSO and a random forest model to output a core feature subset, the model building module builds a logic regression architecture to calculate a baseline logarithm probability, and the dynamic updating module outputs a real-time risk probability in combination with follow-up visit environment data, a time adjustment coefficient and the baseline logarithm probability. And the interactive output module outputs a risk layering label and a feature contribution degree. According to the method, redundancy is eliminated through double-layer screening, the time dimension is introduced to adjust the real-time correction probability, visual attribution is realized in combination with the SHAP algorithm, and the dynamic monitoring capability and interpretability are improved.