A Machine Learning System to Improve Heart Failure Patient Assistance

作者：Guidi Gabriele^*; Pettenati Maria Chiara; Melillo Paolo; Iadanza Ernesto

来源：IEEE Journal of Biomedical and Health Informatics, 2014, 18(6): 1750-1756.

DOI：10.1109/JBHI.2014.2337752

• Heart failure (HF)
• machine learning
• telemonitoring

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%26apos; 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 %26quot;supervised database%26quot; 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.

• 出版日期2014-11