Machine learning approaches for supporting patient-specific cardiac rehabilitation programs

Abstract

Cardiac rehabilitation is a well-recognised non-pharmacological intervention that prevents the recurrence of cardiovascular events. Previous studies investigated the application of data mining techniques for the prediction of the rehabilitation outcome in terms of physical, but fewer reports are focused on using predictive models to support clinicians in the choice of a patient-specific rehabilitative treatment path. Aim of the work was to derive a prediction model for help clinicians in the prescription of the rehabilitation program. We enrolled 129 patients admitted for cardiac rehabilitation after a major cardiovascular event. Data on anthropometric measures, surgical procedure and complications, comorbidities and physical performance scales were collected at admission. The prediction outcome was the rehabilitation program divided in four different paths. Different algorithms were tested to find the best predictive model. Models performance were measured by prediction accuracy. Mean model accuracy was 0.790 (SD 0.118). Best model selected was Lasso regression showing an average classification accuracy on test set of0.935. Data mining techniques have shown to be a reliable tool for support clinicians in the decision of cardiac rehabilitation treatment path.