Predictive Models for Risk Assessment of Worsening Events in Chronic Heart Failure Patients

This work aims at developing and assessing a machine learning based Knowledge Discovery task for risk prediction of major cardiovascular worsening events in chronic heart failure patients. Clinical data from 50patients with chronic heartfailure was analyzed. For each patient, personal data, different vital and clinical parameters and the presence of cardiovascular worsening events have been stored every three months per two years. We defined the Knowledge Discovery analysis as a predictive task stated as supervised binary classification problem. The class label was defined based on the occurrence or not of cardiovascular worsening events between two consecutive visits. To take into account the temporality of the worsening events, six different temporal weighting strategies, applied to the vital parameters, were tested. Several machine learning algorithms were applied for each strategy obtaining different predictive models. Models performance have been evaluated mainly in term of area under the ROC curve (AUC), and Linear Support Vector Machine got the best performing predictive model. The implemented Knowledge Discovery task have shown to be a reliable tool for support cardiologists for riskpredictions of major cardiovascular worsening events.