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All Journal Journal Medical Informatics Technology
Chairul, Yasrizal
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Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health

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Relevance of e-Health Needs and Usage in Indonesia Chairul, Yasrizal; Aziz, Faruq; Hadianti, Sri
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.20

Abstract

The eHealth application can be used for healthcare, supervision, literature, education, and research. It is a cost-efficient and secure application based on information and communication technology for the health and medical fields. The use of Information and Communication Technology (ICT) as an infrastructure or medium that connects hospitals and health centers using the eHealth electronic health application is the key problem facing the implementation of eHealth on a worldwide scale. eHealth is an ICT-based application for the healthcare industry and one of the Action Plans of the World Summit on the Information Society (WSIS) Geneva 2003. The goal of using the eHealth app is to increase patient access, medical process efficiency, effectiveness, and process quality. This covers the administration of medical services provided by hospitals, clinics, health centers, medical professionals (including therapists and doctors), laboratories, pharmacies, and insurance
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 Aziz, Faruq Muhammad Haris Sri Hadianti