Applications of Machine Learning for Predicting Heart Failure

Abstract

This chapter provides an introduction to the use of machine learning (ML) for the diagnosis of heart failure (HF). ML is the field responsible for developing methods and tools that can learn and make decisions based on data. The growing number of HF patients and increasing healthcare costs indicate the importance of the early diagnosis of HF for efficient treatment planning. The chapter considers the example of HF diagnosis using electrocardiogram (ECG) data. ECGs are performed in addition to physical examination and disease history investigation of the patient. ML has gained a growing importance in cardiovascular medicine, especially for the detection and diagnosis of HF. Based on the nature of the ML algorithms used for detection and diagnosis of HF, four classes can be identified: supervised learning models, unsupervised learning models, semi-supervised learning models, and reinforcement learning models. The use of electronic health record is an important research direction for predicting HF.