A COMPARATIVE STUDY OF BIG DATA MINING ALGORITHMS FOR EARLY DETECTION OF HEART ATTACK RISK FACTORS IN ELECTRONIC MEDICAL RECORDS
Keywords:
Heart Attack Risk Prediction, Big Data Mining, Random Forest, Support Vector Machine, Electronic Medical Records, Machine Learning In Healthcare, Cardiovascular Disease, Early DetectionAbstract
The early detection of heart attack risk factors is critical for reducing cardiovascular disease-related mortality. This study presents a comparative analysis of five big data mining algorithms—Decision Tree, Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nearest Neighbors (KNN)—using Electronic Medical Records (EMRs) to predict heart attack risk. The results show that Random Forest outperformed the other models, achieving the highest accuracy, precision, recall, and F1-score. SVM also demonstrated strong performance, while KNN lagged behind in both accuracy and prediction efficiency. The findings suggest that advanced machine learning models, particularly Random Forest, offer significant potential for improving early detection and healthcare decision-making.
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