Construction of a multilabel prediction model for adverse outcomes in patients with unstable angina pectoris
Wang Ziyun
Zhang Yu
Han Gangfei
Yan Jingjing
Tian Jing
Abstract：Objective To construct a multilabel prediction model for processing multilabel data and predicting adverse outcomes in patients with unstable angina pectoris(UAP)by applying algorithm of multilabel synthetic minority over sampling technique(MLSMOTE).Methods UAP patients were chosen from the Second Hospital of Shanxi Medical University from Jan.2017 to May 2020.Patients'information was collected by using a retrospective and prospective clinical cohort study.The multilabel feature subsets were selected by using algorithm of relief F for multilabel feature selection(RF-ML)taken myocardial infarction(MI),heart failure(HF),revascularization,stroke and death as outcomes in UAP patients.MLSMOTE algorithm is used to deal with multilabel imbalance,and on this basis,multilabel classification models of classifier chains(CC)were constructed and compared,and random forest,naive Bayes,support vector machine(SVM)and K-nearest neighbor(KNN)were selected as base classifiers,and the model performance was reviewed.Results The were finally 18 variables screened and enclosed into the model by using RF-ML method.These variables included uric acid(UA),creatinine(Cr),platelet,chlorine(CL),hemoglobin(Hb),systolic blood pressure(SBP),diastolic blood pressure(DBP),heart rate(HR),sodium(Na),total bilirubin(TBIL),indirect bilirubin(IBIL),albumin(ALB),total bile acid(TBA),body mass index(BMI),blood sugar,direct bilirubin(DBIL),low-density lipoprotein-cholesterol(LDL-C)and high-density lipoprotein-cholesterol(HDL-C).The imbalanced processing was carried out to 5 labels involved in this study including MI,HF,revascularization,stroke and death by using MLSMOTE.The CC models were constructed with random forest,naive Bayes,SVM and KNN as base classifiers by using processed data,and the results showed that the performance of CC model with naive Bayes as base classifier was better in 6 indexes of ranking loss,macro-AUC,micro-AUC,macro_F1,micro_F1 and macro-recall than that of other CC models.Conclusion MLSMOTE algorithm is used for imbalance processing in this study,which improves the imbalance rate of the original labels to some extent.The CC models are constructed by using balanced data,fully considering the specific features and correlation of labels.The performance of CC model with naive Bayes as base classifier was the best.
Keywords：Unstable angina pectorisMultilabel feature selectionMultilabel imbalanceLabel specific features
Publication Date：2024-06-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 651-656 )