Optimizing Heart Disease Prediction Accuracy using Machine Learning Models

Abstract

Heart disease remains a major global health challenge and a leading cause of death. Early detection is critical, as untreated conditions can progress rapidly, leading to severe outcomes. Advances in modern medicine, such as electronic health records and connected medical devices, enable continuous health monitoring. However, analyzing the vast data generated by these technologies requires advanced data mining techniques to effectively classify health information and prioritize heart disease detection. Despite these tools, early diagnosis remains a significant hurdle for medical professionals. To address this, accurate and timely prediction systems are essential for saving lives. Our approach emphasizes thorough data preprocessing, including handling missing values, normalization, and encoding categorical features. We employ a range of machine learning algorithms, from traditional methods to advanced models, refining their performance through extensive experimentation and hyperparameter tuning. Feature selection is a critical component, enhancing model interpretability by identifying key predictors of heart disease risk.