Research of Heart Disease Prediction Based on Machine Learning

Abstract

The use of massive clinical data in the medical field for supporting medical decision support is an inevitable development trend. Medical decision support is based on a variety of data sources accumulated and acquired in real-time in the clinic, and various machine learning algorithms are used to achieve classification of patient disease types or prediction of disease risks. This paper assists in performing cardiac disease prediction starting from different heart disease types (coronary heart disease) and data sets, summarizing the currently adopted machine learning diagnosis and prediction methods, highlighting the characteristics and differences of these methods, and analyzing the challenges and future developments. The results show that machine learning techniques have a wide range of applications in cardiac diseases. However, each machine learning method can only be applied to a specific scope due to the non-uniformity of medical data. At the end of the article, the prediction of heart disease is summarized.