Death Events from Heart Failure Prediction Using Machine Learning Approach

Heart failure is a significant global health concern, contributing to high mortality rates and imposing substantial burdens on healthcare systems. Early prediction of mortality in heart failure patients can facilitate timely interventions, enhance patient management, and improve overall survival outcomes. This study applies machine learning techniques to predict death events among heart failure patients using clinical data. Five classification algorithms—Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbor (KNN), and Gaussian Naïve Bayes—are implemented on a dataset containing 5,000 patient records with 13 clinical attributes obtained from Kaggle. The research methodology includes extensive data preprocessing, such as missing value imputation using mean/mode strategies, standardization, feature selection via ANOVA P-value testing, and data balancing with the Synthetic Minority Over-sampling Technique (SMOTE). Model optimization was performed through hyperparameter tuning and cross-validation to enhance predictive accuracy. The results from two experimental settings—one without optimization and one with hyperparameter tuning, feature selection, and Principal Component Analysis (PCA)—show that K-Nearest Neighbor achieved the highest accuracy (98.5%) and precision (98.9%) after optimization. In contrast, Random Forest performed exceptionally well without tuning, achieving an accuracy of 99.2% and an F1-score of 98.7%. The findings demonstrate the effectiveness of machine learning in heart failure prognosis, providing valuable insights for clinical decision-making and personalized patient care.