Heart Failure Prediction Using Machine Learning

Abstract

This study explores the use of XGBoost and Shapley values for heart failure prediction, in addition to processing models such as neural network, CNN, and SVM. By comparing the performance of these different machine learning algorithms, we aim to develop a robust and accurate predictive model for identifying individuals at risk of heart failure. Our findings highlight the strengths and limitations of each approach, providing valuable insights for future research in this area. Ultimately, this research aims to improve the early detection and management of heart failure, leading to better patient outcomes and more efficient healthcare practices. Key Words: heart failure prediction, XGBoost, SHAPley values, neural network, CNN, SVM, machine learning algorithms, early detection, death events.