Evaluation of Machine Learning Methods for Predicting Heart Failure Readmissions: A Comparative Analysis

Abstract

A heart failure (HF) condition is a type of chronic cardiovascular disease that affects millions of people globally. It can lead to various symptoms and has a significant impact on the quality of life. Despite the advancements that have been made in treating this condition, it remains a major public health issue. One of the biggest challenges that HF management faces is the high number of readmissions. This issue contributes to the increasing of patients' outcomes and costs the healthcare system. Implementing effective interventions and identifying those at high risk of returning to the hospital can help lower the financial burden on the system. Through the use of machine learning techniques, researchers can now predict the likelihood of HF readmissions. These tools can analyze large datasets and provide a personalized diagnosis and treatment plan. There have been various studies that have examined the use of ML for predicting HF readmissions. The goal of this study is to analyze the various techniques used in predicting HF readmissions and provide a comprehensive analysis of their performance. Through a combination of data collected from various sources, including a diverse set of patients, we will be able to explore the performance of various ML algorithms. In addition to the algorithms' performance, we will also look into their impact on various parameters, such as model evaluation metrics, optimization techniques, and feature selection. The findings of this study will be used to inform policymakers and healthcare providers about the use of ML techniques to identify patients at high risk of HF readmissions. These insights can help them improve the quality of care for those with this condition and develop effective interventions. The objective of this study is to use the power of ML to improve the management of HF and reduce the burden of readmissions on both the patients and the healthcare systems.