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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Teknoinfo JIKA (Jurnal Informatika) Bulletin of Computer Science Research Insearch: Information System Research Journal Journal of Computing and Informatics Research
Heriyani, Nofitri
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences Other

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Journal : Bulletin of Computer Science Research

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Pendekatan Hybrid K-Means SMOTE dan Logistic Regression Untuk Deteksi Dini Diabetes Mellitus Pada Imbalanced Data Salam, Abdus; Azhari, Lukman; Septarini, Ri Sabti; Heriyani, Nofitri
Bulletin of Computer Science Research Vol. 5 No. 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i3.502

Abstract

The increasing global prevalence of Diabetes Mellitus necessitates more accurate early detection efforts, particularly through machine learning-based approaches. However, one of the main challenges in medical classification lies in data imbalance, where the number of diabetic cases is significantly lower than that of non-diabetic ones. This study aims to develop a hybrid model by integrating Logistic Regression and K-Means SMOTE to enhance the sensitivity of early detection for Diabetes Mellitus, especially toward the minority class. Logistic Regression is chosen for its computational efficiency and interpretability, while K-Means SMOTE plays a role in balancing class distribution by generating synthetic samples in a structured manner based on clusters of minority class data. The dataset used consists of 2,000 records with 9 health-related features, obtained from the Kaggle platform. Evaluation results indicate that the model utilizing K-Means SMOTE achieves the best performance, with an accuracy of 82.00%, an F1-score of 72.73% for the Diabetes class, and the highest ROC-AUC score of 87.48%. Compared to models without oversampling and with standard SMOTE, this approach improves model generalization and sensitivity to positive cases. These findings have practical implications for the development of fairer and more effective machine learning-based early detection systems, particularly for implementation in healthcare facilities with limited resources.
Co-Authors Abdus Salam, Abdus Azhari, Lukman Handayani, Nurdiana Jafar Rumandan, Rhaishudin Lasiyono, M. Munawir Luqmanul Hakim Moh. Erkamim Mulyadi Mulyadi Nurhasan Nugroho Sah, Andrian Septarini, Ri Sabti Soares, Teotino Gomes Tonggiroh, Mursalim Yanuardi Yanuardi