Improving Coronary Heart Disease Prediction Using Random Forest with a Modified Minority Synthetic Oversampling Technique on an Imbalanced Dataset

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 3

Year of Publication : 2025

Authors : M. Janaki Ramudu, K. Narasimha Raju, A. Krishna Mohan

10.14445/23488379/IJEEE-V12I3P111

How to Cite?

M. Janaki Ramudu, K. Narasimha Raju, A. Krishna Mohan, "Improving Coronary Heart Disease Prediction Using Random Forest with a Modified Minority Synthetic Oversampling Technique on an Imbalanced Dataset," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 3, pp. 100-113, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I3P111

Abstract:

Coronary Heart Diseases (CHDs) are the leading cause of death, with a fatal rate increasing every year. Around 80 million females and 110 million males are afflicted by this illness across the globe. Early detection and accurate risk assessment of this disease remain crucial in medical research. Many researchers are working on this issue, but it remains challenging. The proposed technique predicts CHD by applying the Modified Minority Synthetic Over-Sampling Technique (MMSOT) to balance the data and classify the data using the Random Forest (RF) and grid search techniques to fine-tune the hyperparameters. The proposed technique achieved decent performance on the Comprehensive Heart Disease Dataset, with an accuracy of 94.84%, ROC-AUC of 98.15%, Sensitivity of 95.00%, Specificity of 94.70%, F1-Score of 94.61%, Precision (PPV) of 94.21%, and NPV of 95.42%, outperforming baseline models.

Keywords:

Coronary Heart Disease, Grid Search, Machine Learning Techniques, MMSOT, SMOTE.

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