A Proactive Approach in Predicting Heart Attacks Using Hyperparameter Tuning

Abstract

Heart attacks are a significant global health concern, often resulting in severe health implications and mortality. Early detection and accurate prediction of cardiac arrest play a crucial role in improving patient outcomes and reducing healthcare costs. In this study, we employ advanced machine learning algorithms includes learning method i.e, Hyperparameter tuning in Recursive Feature Elimination with Cross-Validation(RFECV) on XGBoost, Linear Regression(LR), Support Vector Machines(SVM),K-Nearest Neighbors(KNN), Bootstrap Aggregation, CART to predict the likelihood of a heart attack based on relevant medical data. We utilize a comprehensive dataset encompassing various factors to train and evaluate the models. The comparative analysis of these machine learning algorithms provides valuable insights into their respective performance, aiding in the identification of an effective predictive model for cardiac arrest. Ultimately, this research contributes to the advancement of predictive healthcare analytics, potentially revolutionizing the way predicting cardiac arrest is assessed and mitigated. This article delves into the field of cardiac arrest prediction,exploring the current advancements, methodologies, and potential implications for improving patient outcomes and healthcare strategies.