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All Journal AMALIAH: JURNAL PENGABDIAN KEPADA MASYARAKAT Abdi Kami : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Informatics and Electronics Engineering Coreid Journal Multifinance
Perdana, Rangga Satria
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Journal : Jurnal Teknik Informatika (JUTIF)

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Improving Extreme Gradient Boosting Model for Heart Disease Prediction Using SMOTE for Class Imbalance Rohmayani, Dini; Sugianto, Castaka Agus; Perdana, Rangga Satria; Nafea, Mohammed Mansoor
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4753

Abstract

The goal of this study is to come up with an intelligent predictive model that can classify the severity of heart disease. The model will employ both XGBoost and oversampling to resolve the problem of data imbalance. In addition, the model will be implemented for real-world application using an interactive interface. The study uses the UCI Heart Disease dataset, which includes many clinical features. Preprocessing involves handling missing values, removal of features with a substantial fraction of missing values, and the use of SMOTE resampling for learning from class-balanced instances. The main classifier that was used for the research purposes was the XGBoost classifier, while the dataset was split 80:20 for training and testing purposes. For ease of individual-level real-time testing of the predictions, the model is implemented through Streamlit. The XGBoost model worked extraordinarily well, with the accuracy standing at 92%, as did precision along with recall, as well as the F1-score, being 92%. These findings clearly outperform other current studies of the same sort that have made use of alternative classifiers. In addition, its deployment using Streamlit makes it even more clinically applicable. Innovation The novelty of the research lies in the combined application of SMOTE with XGBoost, enabling effective classification under imbalanced conditions, along with the real-time implementation using Streamlit for user-level predictions. The model is of high value for early identification and stratification of the severity of heart disease in clinical decision support settings.
Co-Authors Beki Subaeki, Fatkhan Gunawan, Aldy Rialdy Atmadja, Beki Dwijayanti , Rima Erna Garnia Fahrulrozi, Muhamad Gunawan, Yanyan Hady Siti Hadijah, Hady Siti Heri Purwanto Jaelani, Mochamad Pitra Khaerul Manaf Lisaumi, Siti Riyyan Lusiana, Senny Maharani, Lisya Muhammad, Farrel Febrian Nafea, Mohammed Mansoor Noerzaeny, Ihsan Nurwathi, Nurwathi Rohmayani, Dini Setiadi, Herdi Sugianto, Castaka Agus Tito Ramadhan, Muhammad Wahyuni, Astri Sri Wiharko, Teguh