Heart Disease Predictive Modeling with XGBoost and SMOTE-Driven Class Imbalance Mitigation

T. Roopa; Ganesh Dalappagari Ramanjinappa

doi:10.48084/etasr.14301

Authors

T. Roopa School of Computing & Information Technology, REVA University, Bengaluru, Karnataka, India | SSIT, SSAHE University, Tumkur, India
Ganesh Dalappagari Ramanjinappa School of Computing and Information Technology, REVA University, Bengaluru, Karnataka, India

Volume: 15 | Issue: 6 | Pages: 29914-29918 | December 2025 | https://doi.org/10.48084/etasr.14301

Received: 25 August 2025 | Revised: 14 September 2025, 16 September 2025, and 20 September 2025 | Accepted: 21 September 2025 | Online: 24 October 2025

Corresponding author: T. Roopa

Abstract

Heart disease is one of the leading causes of death worldwide, highlighting the importance of early and precise diagnosis techniques. Clinical and demographic data can be analyzed using machine learning techniques to detect heart disease. This study combines XGBoost with the Synthetic Minority Oversampling Technique (SMOTE), introducing a strong prediction framework that handles class imbalance in the dataset. This study used 303 patient records with 13 clinical features from the UCI Heart Disease dataset, involving preprocessing, including addressing missing values, categorical variable one-hot encoding, and standardization of numeric features, followed by hyperparameter optimization using GridSearchCV. According to experimental findings, the model achieved an overall accuracy of 90%, with true positive and true negative counts of 96 and 66, respectively. The classification report shows good precision (0.87–0.93), recall (0.83–0.95), and F1-score (0.88–0.91), with low misclassification rates. Age, the type of chest pain, maximum heart rate, and cholesterol levels are highlighted as important predictors in the feature importance evaluation. According to the results, the XGBoost+SMOTE pipeline is very successful at classifying binary heart disease and can help with early therapeutic intervention techniques, which may lead to better patient outcomes.

Keywords:

heart disease, machine learning, XGBoost, SMOTE, clinical diagnosis

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