Heart Disease Prediction Using Machine Learning Algorithms

Heart disease stands as a leading cause of mortality, presenting significant challenges in clinical data analysis for accurate prediction. However, modern advancements in machine learning (ML) have shown immense promise in leveraging vast healthcare datasets to predict and manage heart disease effectively. ML algorithms have exhibited exceptional efficacy in discerning intricate patterns within patient data, aiding in both early diagnosis and the amelioration of existing cardiac conditions. Despite widespread application of ML in various sectors, its utilization in forecasting cardiac illness remains relatively understudied. This paper introduces an innovative approach aimed at refining heart disease prediction accuracy through the strategic utilization of ML techniques to identify pivotal features. This study aims to create a reliable prediction model by examining various feature combinations and utilizing well-known classification techniques like decision trees, logistic regression, random forest, K-nearest neighbors (KNN), gradient boosting machines (GBM), XG Boost, and multilayer perceptrons. Through this comprehensive exploration, we seek to contribute to the enhancement of cardiac health prediction, thereby potentially improving patient outcomes and reducing the burden of heart disease on global healthcare systems.