International journal of medical informatics · 2025
Developing a multi-label learning model to predict major adverse cardiovascular events in patients with unstable angina pectoris: A prospective cohort study.
СтатьяНаблюдательное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Li Jing, Yang Hong, Zhang Yu, Yan Jingjing, Tian Jing, Zhang Yanbo
Аннотация
BACKGROUND: Major adverse cardiovascular events (MACE) represent critical endpoints in cardiovascular research. The occurrence of MACE in patients with unstable angina pectoris (UAP) exhibits multidimensional complexity. We employed multi-label learning (MLL) models to concurrently predict five distinct types of MACE.
METHODS: This prospective observational cohort study analysed the 978 UAP patients from the Second Affiliated Hospital of Shanxi Medical University (Taiyuan, China) between July 1, 2017, and June 30, 2019. Three-year follow-up endpoints encompassed all-cause death, heart failure, stroke, myocardial infarction, and revascularization. We utilized ReliefF for Multi-label Feature Selection (RFML), Mutual Information-based Feature Selection (MIFS), and Scalable Criteria for Large label Set (SCLS) to identify significant prognostic variables. Nineteen MLL models were implemented, including Binary Relevance (BR), Classifier Chains (CC), Label Powerset (LP), Random k-Labelsets (RAkEL), Multi-label k-Nearest Neighbor, Twin Support Vector Machine to Multi-label Learning, and Wrapping multi-label learning with label-specific features generation. BR, CC, LP, and RAkEL models were constructed using four base classifiers: Decision Tree, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine. Performance evaluation incorporated 12 different metrics.
RESULTS: The RFML, MIFS, and SCLS respectively screened 18, 12, and 14 important features, and the MLL prediction performance based on RFML selected features was the best. Among the MLL models, RAkEL with Random Forest as the base classifier demonstrated superior predictive performance, achieving an Accuracy of 0.575 ± 0.022, Precision of 0.646 ± 0.029, Hamming loss of 0.159 ± 0.008, One error of 0.425 ± 0.022, Macro_F1 of 0.719 ± 0.028, Micro_F1 of 0.740 ± 0.011, Macro_AUC of 0.786 ± 0.031, Micro_AUC of 0.806 ± 0.030 and Multi-Brier Score of 0.115 ± 0.035.
CONCLUSIONS: The RAkEL model with Random Forest as the base classifier significantly enhanced predictive accuracy for MACE in UAP patients. This approach provides a more comprehensive risk assessment, enabling clinicians to develop personalized treatment strategies and improve patient outcomes.
METHODS: This prospective observational cohort study analysed the 978 UAP patients from the Second Affiliated Hospital of Shanxi Medical University (Taiyuan, China) between July 1, 2017, and June 30, 2019. Three-year follow-up endpoints encompassed all-cause death, heart failure, stroke, myocardial infarction, and revascularization. We utilized ReliefF for Multi-label Feature Selection (RFML), Mutual Information-based Feature Selection (MIFS), and Scalable Criteria for Large label Set (SCLS) to identify significant prognostic variables. Nineteen MLL models were implemented, including Binary Relevance (BR), Classifier Chains (CC), Label Powerset (LP), Random k-Labelsets (RAkEL), Multi-label k-Nearest Neighbor, Twin Support Vector Machine to Multi-label Learning, and Wrapping multi-label learning with label-specific features generation. BR, CC, LP, and RAkEL models were constructed using four base classifiers: Decision Tree, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine. Performance evaluation incorporated 12 different metrics.
RESULTS: The RFML, MIFS, and SCLS respectively screened 18, 12, and 14 important features, and the MLL prediction performance based on RFML selected features was the best. Among the MLL models, RAkEL with Random Forest as the base classifier demonstrated superior predictive performance, achieving an Accuracy of 0.575 ± 0.022, Precision of 0.646 ± 0.029, Hamming loss of 0.159 ± 0.008, One error of 0.425 ± 0.022, Macro_F1 of 0.719 ± 0.028, Micro_F1 of 0.740 ± 0.011, Macro_AUC of 0.786 ± 0.031, Micro_AUC of 0.806 ± 0.030 and Multi-Brier Score of 0.115 ± 0.035.
CONCLUSIONS: The RAkEL model with Random Forest as the base classifier significantly enhanced predictive accuracy for MACE in UAP patients. This approach provides a more comprehensive risk assessment, enabling clinicians to develop personalized treatment strategies and improve patient outcomes.