Machine learning-based predictive modeling of angina pectoris in an elderly community-dwelling population: Results from the PoCOsteo study.

ABSTRACT:

Background

Angina pectoris, a comparatively common complaint among older adults, is a critical warning sign of underlying coronary heart disease. We aimed to develop machine learning-based models using multiple algorithms to predict and identify the predictors of angina pectoris in an elderly community-dwelling population.

Methods

Medical records of 2000 participants in the PoCOsteo study between 2018 and 2021 were analyzed. The Rose Angina Questionnaire was used to indicate angina pectoris. Preprocessing was performed using imputation and scaling methods. We developed the following models: logistic regression (LR), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (KNN), linear and quadratic discriminant analysis (LDA, QDA), decision tree (DT),

SUBMITTER: Mousavi S 

PROVIDER: S-EPMC12324099 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature