Development of Machine Learning Models for Predicting Effectiveness and Adherence in Cardiac Rehabilitation

Cardiac rehabilitation (CR) programs are vital for people recovering from cardiac surgeries or events. However, the effectiveness of CR programs varies and some patients may not adhere to them, which might result in less favourable outcomes. Machine Learning (ML) models could help predict the effectiveness and adherence of CR programs. This study proposes two such models: a) the CR program effectiveness prediction model and b) the CR program adherence prediction model. The models were trained on data from retrospective cohort study with 1448 participants collected at the Cardiac Rehabilitation Unit of the Hospital Clinico de Santiago de Compostela in Galicia, Spain (SERGAS). Data cleaning, normalization, imputation, statistical analysis, feature selection and repeated stratified k-fold cross-validation (CV) were applied on the ML pipeline, which tested and evaluated on baseline demographic, clinical, exercise tests and behavioral features. The performance of ML models was assessed by mean Area Under operating characteristic Curve (AUC), specificity, sensitivity, and balanced accuracy with 95% confidence interval (CI). The results show that Random Forest (RF) outperformed other evaluated classifiers for the CR program effectiveness model, with the highest AUC value of 0.789 (0.775, 0.802), while the best classifier for the CR adherence model was the Logistic Regression (LR) classifier, with an AUC value of 0.757 (0.749, 0.764). SHAP plots were also used to investigate the relationships among the variables used in the analysis. Finally, a two-dimensional scoring system was developed to jointly assess predicted adherence and effectiveness, enabling personalized visualization of patient response to CR.