Comparative Analysis of Machine Learning Algorithms for Predicting Heart Attack
DOI:
https://doi.org/10.57152/ijatis.v3i1.2514Keywords:
Decision Tree, Extreme Gradient Boosting, Heart Attack, Random Forest, Support Vector MachineAbstract
Early detection of heart attack risk is crucial for reducing mortality rates associated with cardiovascular diseases. This study aims to perform a comparative performance analysis of four machine learning algorithms: Decision Tree, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) in classifying heart attack risk using a clinical dataset from Kaggle. The research methodology includes data preprocessing, data splitting using a 70:30 hold-out scheme, and model evaluation through a confusion matrix and standard classification metrics. The test results indicate that Random Forest provides the superior performance with the highest accuracy of 84%. Meanwhile, the SVM and XGBoost algorithms achieved 80% accuracy each, while the Decision Tree achieved the lowest at 70%. These findings confirm that ensemble-based models like Random Forests exhibit greater stability in handling complex clinical data patterns, making them highly promising for integration into early heart health warning systems.
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