Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients.

The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts.

Networking and data sharing reduces hospitalization cost of heart failure: the experience of GISC study.

Rationale, Aims And Objectives: Heart failure (HF) is a concerning public health burden in Western society because, despite the improvement of medical treatments, it is still associated with adverse outcomes (high morbidity and mortality), resulting in one of the most expensive chronic disease in Western countries. Hospital admission particularly is the most expensive cost driver among the several resources involved in the management of HF. The aim of our study was to investigate the cost of hospitalization before and after the enrolment to a new strategy (GISC) in the management of patients with HF.