Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Evanthia E Tripoliti; Theofilos G Papadopoulos; Georgia S Karanasiou; Katerina K Naka; Dimitrios I Fotiadis

doi:10.1016/j.csbj.2016.11.001

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Comput Struct Biotechnol J. 2016 Nov 17:15:26-47. doi: 10.1016/j.csbj.2016.11.001. eCollection 2017.

Authors

Evanthia E Tripoliti¹, Theofilos G Papadopoulos², Georgia S Karanasiou¹, Katerina K Naka³, Dimitrios I Fotiadis¹

Affiliations

¹ Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, GR 45110 Ioannina, Greece; Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece.
² Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, GR 45110 Ioannina, Greece.
³ Michaelidion Cardiac Center, University of Ioannina, GR 45110 Ioannina, Greece; 2nd Department of Cardiology, University of Ioannina, GR 45110 Ioannina, Greece.

Abstract

Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3-5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.

Keywords: Classification; Data mining; Diagnosis; Heart failure; Prediction; Severity estimation.

Publication types

Review