Machine Learning Approaches for Healthcare Analytics: A Comparative Study

Abstract

The rapid growth of healthcare data has led to a significant interest in developing machine learning models for healthcare analytics. In this study, we compare the performance of several machine learning approaches for healthcare analytics, including decision trees, support vector machines, neural networks, and random forests. The study was conducted using a large dataset of electronic health records (EHRs) from a major hospital in the United States.