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

ABSTRACT: 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. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the

SUBMITTER: Lorenzoni G 

PROVIDER: S-EPMC6780582 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature