title: A machine learning system to improve heart failure patient assistance.
abstract: In this paper, we present a clinical decision support system (CDSS) for the analysis of heart failure (HF) patients, providing various outputs such as an HF severity evaluation, HF-type prediction, as well as a management interface that compares the different patients' follow-ups. The whole system is composed of a part of intelligent core and of an HF special-purpose management tool also providing the function to act as interface for the artificial intelligence training and use. To implement the smart intelligent functions, we adopted a machine learning approach. In this paper, we compare the performance of a neural network (NN), a support vector machine, a system with fuzzy rules genetically produced, and a classification and regression tree and its direct evolution, which is the random forest, in analyzing our database. Best performances in both HF severity evaluation and HF-type prediction functions are obtained by using the random forest algorithm. The management tool allows the cardiologist to populate a "supervised database" suitable for machine learning during his or her regular outpatient consultations. The idea comes from the fact that in literature there are a few databases of this type, and they are not scalable to our case.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Guidi
authors.forename: Maria Chiara
authors.initials: MC
authors.lastname: Pettenati
authors.forename: Paolo
authors.initials: P
authors.lastname: Melillo
authors.forename: Ernesto
authors.initials: E
authors.lastname: Iadanza
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D017143:Fuzzy Logic; D006333:Heart Failure / Q000503:physiopathology*; D006801:Humans; D020097:Natriuretic Peptide, Brain; D015203:Reproducibility of Results; D013318:Stroke Volume; D017216:Telemedicine / Q000379:methods*
chemical_list: D020097:Natriuretic Peptide, Brain