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Comparative Analysis of Machine Learning Algorithms for Heart Disease Prediction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 5 |
| Year of Publication: 2025 |
| Authors: Mohammad Belal Aziz, Syed Wajahat Abbas Rizvi |
10.5120/ijca2025924890
|
Mohammad Belal Aziz, Syed Wajahat Abbas Rizvi . Comparative Analysis of Machine Learning Algorithms for Heart Disease Prediction. International Journal of Computer Applications. 187, 5 ( May 2025), 62-65. DOI=10.5120/ijca2025924890
Abstract
Diagnosing heart disease in medical facilities is of high importance as it is the leading cause of death and requires precise predictive algorithms. The seven classification techniques considered in the research are Logistic Regression, Decision Tree, Random Forest, SVM, XGBoost, Stacking Ensemble, Neural Network. The study measured models based on ROC-AUC, accuracy and recall among the chief metrics across both the model training and test evaluations. All models saw their peak of accuracy at 84.53%, with Logistic Regression proving to be the most accurate of the Random Forest and SVM. During evaluation, heart disease cases detection was far from optimal – Decision Trees reported 27% recall and Neural Networks a paltry 10%. The study reveals that organizations experience a tradeoff between accuracy and recall which underscores the need of using tactics such as ensemble learning and data augmentation to achieve superior sensitivity performance. Investigations will attempt to enhance the case detection rates while still upholding high predictive power applicable to medicine.
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