SMART HEALTH MONITORING SYSTEM: HEART ATTACK RISK PREDICTION USING MACHINE LEARNING
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Heart disease remains one of the leading causes of mortality worldwide. Early prediction of heart attack risk is crucial for preventive healthcare. This paper presents a machine learning-based system to predict the risk of heart attack using patient health and lifestyle data. A dataset of 8,763 records with 26 attributes was employed, encompassing factors such as age, cholesterol, blood pressure, heart rate, diabetes, smoking, obesity, exercise hours, stress levels, and BMI. Extensive data preprocessing, feature selection using the chi-square test, and three classification algorithms—Logistic Regression, Decision Tree, and Random Forest—were implemented and compared. Logistic Regression achieved the highest accuracy of 65.14%, followed by Random Forest at 60.69% and Decision Tree at 53.90%. A prediction function was developed to classify new patient data as either high or low risk in real time. The results demonstrate the potential of machine learning in supporting early cardiac risk assessment.
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V, S. & R, T. (2026). Smart Health Monitoring System: Heart Attack Risk Prediction using Machine Learning. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.017
V, Suriya, and Tamilarasan R. "Smart Health Monitoring System: Heart Attack Risk Prediction using Machine Learning." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.017.
V, Suriya, and Tamilarasan R. "Smart Health Monitoring System: Heart Attack Risk Prediction using Machine Learning." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.017.
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