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All Journal El-Iqtishady Iqtishaduna : Jurnal Ilmiah Mahasiswa Hukum Ekonomi Syariah Journal of Islamic and Law Studies (JILS) Journal Medical Informatics Technology Journal of International Multidisciplinary Research Tafaqquh : Jurnal Hukum Ekonomi Syariah dan Ahwal Syahsiyah Landraad Jurnal Ilmiah Keagamaan dan Kemasyarakatan Indonesian Journal of Islamic Jurisprudence, Economic and Legal Theory Interdisciplinary Explorations in Research Journal (IERJ) Scientiva: Journal of Society and Scientific Studies
Muhammad Haris
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Religion Arts Humanities Computer Science & IT Dentistry Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Journal Medical Informatics Technology

 1 
Early Diabetes Detection Using Machine Learning Models: A Case Study from Indonesian Clinical Data Chairul, Yasrizal; Muhammad Haris
Journal Medical Informatics Technology Volume 4 No. 1, March 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i1.80

Abstract

Diabetes is a major health problem that can significantly reduce life expectancy and increase the risk of serious complications such as kidney failure, stroke, and cardiovascular disease. Therefore, early detection is essential to prevent the progression of the disease. This study proposes a machine learning-based approach for early diabetes detection using a private dataset obtained from RSUP Persahabatan General Hospital in Jakarta, Indonesia. The dataset consists of 501 patient records with clinical and laboratory features extracted from the hospital’s electronic medical record system. Several machine learning algorithms were implemented and compared, including Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, Naïve Bayes, Extreme Gradient Boosting, Ensemble methods, and Artificial Neural Networks. Feature selection was performed using ANOVA, and hyperparameter optimization was applied using GridSearchCV to improve model performance. The experimental results show that the Artificial Neural Network model achieved the best performance with an accuracy of 0.86 (86%). Statistical analysis using logistic regression identified systolic blood pressure, diastolic blood pressure, age, HDL cholesterol, and leukocyte levels as the most significant risk factors associated with diabetes. These findings demonstrate the potential of machine learning techniques to support early diabetes detection using clinical data from Indonesian healthcare settings.
Co-Authors Agus Aditya Arisandi Saputra Akhmad Wafi Almejiem Aditya Wijaya Amelia Fatmawati Amelia Rahmaniah Annajiyah Mansyuroh Budi Rahmat Hakim Chairul, Yasrizal Dian May Syifa Dini Awwalia Fajrul Ilmi Fuad Luthfi Hidayatul Husna Layli Nor Syifa M. Rizki Adi Pratama Mayasari Miswari Miswari Muhammad Mustangin Muhammad Noor Ridani Muhammad Rangga Noor Fadela Noor Halimah Nor Annisa Nur Azizah Nur Azizah, Raudah Nurhalisa Rabiatul Adawiah Rahmat Sholihin Raudhatul Jannah Rizqa Ananda Siti Alpisah Siti Nur Annisa Syarif Hidayat USWATUN HASANAH Yamani Naufal