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Dwiza Riana
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Journal Medical Informatics Technology
Published by Asia Pacific Network for Sustainable Agriculture, Food and Energy
ISSN : 29887003     EISSN : 29887003     DOI : https://doi.org/10.37034/medinftech
Core Subject : Health, Science, Engineering,
Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health
Journal Medical Informatics Technology publishes papers on innovative applications, development of new technologies and efficient solutions in Health Professions, Medicine, Neuroscience, Nursing, Dentistry, Immunology, Pharmacology, Toxicology, Psychology, Pharmaceutics, Medical Records, Disease Informatics, Medical Imaging and scientific research to improve knowledge and practice in the field of Medical.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 72 Documents
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Machine Learning-Based Outcome Prediction in Isolated Ventricular Septal Defects Nurdan Erol; Çiğdem Erol; Ilkim Ecem Emre
Journal Medical Informatics Technology Volume 4 No. 2, June 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i2.151

Abstract

Ventricular Septal Defect (VSD) is one of the most common congenital heart defects. Predicting whether isolated VSD will close spontaneously, require surgical intervention, or remain unclosed is essential for optimizing patient management and avoiding unnecessary treatment. This study aimed to develop and evaluate machine learning (ML) models for predicting VSD outcomes using maternal and neonatal clinical characteristics. A retrospective dataset of 382 patients with isolated VSD was analyzed and categorized into spontaneous closure, surgical closure, and non-closure outcomes. Data preprocessing included duplicate removal and listwise deletion of records with missing values. To address class imbalance, random undersampling and oversampling were applied exclusively to the training set (80%), while the independent test set (20%) remained unchanged. Five ML algorithms-Decision Tree, Random Forest, K-Nearest Neighbor, Naive Bayes, and XGBoost-were evaluated using accuracy, macro-average area under the receiver operating characteristic curve (AUC), and class-specific F1-scores. XGBoost achieved the best overall performance with an accuracy of 65.8% and a macro-average AUC of 0.81, demonstrating balanced classification across all outcome groups. Although Decision Tree and Random Forest produced the highest F1-score (92.3%) for the minority surgical closure class, their overall multiclass performance was inferior to XGBoost. Sampling strategies had minimal impact on overall predictive performance, although ensemble-based methods showed greater robustness to class imbalance. These findings suggest that ML, particularly XGBoost, provides a promising approach for early risk stratification of isolated VSD, supporting personalized clinical decision-making and improving identification of patients requiring surgical intervention.
Mobile Web App Development for Diabetic Foot Screening Using Inlow’s 60-Second Screen with Automated Risk Classification Suhendri; Wildan Zhilal Manafi; Bayu Reviyadi; Sri Rahayu; Iin Karmila Septiani; Mita Nurmala
Journal Medical Informatics Technology Volume 4 No. 2, June 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i2.152

Abstract

Diabetic foot complications constitute a major contributor to preventable lower-extremity amputation, yet primary care screening remains inconsistent due to the absence of integrated digital tools implementing validated clinical protocols. This study presents the design, implementation, and system-centric evaluation of Podiatrix, a mobile web application that operationalizes Inlow's 60-Second Diabetic Foot Screen through an automated, condition-based clinical workflow. Unlike existing tools that address isolated screening criteria, Podiatrix implements all seven Inlow criteria within a unified five-step wizard and applies a deterministic hierarchical classification engine that directly mirrors the original Inlow protocol logic rather than relying on fixed score thresholds. The system was evaluated using three complementary methods: black-box testing across 50 simulated clinical scenarios, Nielsen's heuristic usability evaluation conducted by three independent evaluators, and performance load testing using Apache JMeter under concurrent user conditions. Results demonstrated 100% classification accuracy (50/50 scenarios) matching manual Inlow protocol interpretation, an average heuristic severity score of 1.15 out of 4 indicating high usability, and a mean response time of 820 ms with less than 1% error rate under 100 concurrent users. These findings confirm that Podiatrix provides a computationally robust, highly usable, and scalable digital infrastructure that lays the groundwork for future prospective clinical trials in primary care and community health settings.

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