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All Journal JOIV : International Journal on Informatics Visualization Jurnal Ilmiah Profesi Pendidikan
Almohab, Hadi
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Computer Science & IT Education

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Causal Inference in Observational Studies: Assessing the Impact of Lifestyle Factors on Diabetes Risk Witarsyah, Deden; Almohab, Hadi; A A Abushammala, Haneen
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.1295

Abstract

The global prevalence of type 2 diabetes has escalated in recent decades, prompting an urgent need for effective prevention strategies. Physical activity has emerged as a significant modifiable risk factor for mitigating diabetes risk, yet the precise causal relationship remains a subject of debate, particularly in observational studies. This research leverages advanced causal inference methods to rigorously estimate the effect of physical activity on the risk of developing type 2 diabetes. By employing Propensity Score Matching (PSM), we address confounding biases inherent in observational data, ensuring more reliable estimates of treatment effects. Additionally, we integrate machine learning techniques, including causal forests, to explore heterogeneous treatment effects (HTEs) across different population subgroups. Our findings highlight that the benefits of physical activity in reducing diabetes risk are not uniform but are more pronounced among individuals with higher body mass index (BMI), further underlining the necessity of tailored interventions. The application of advanced causal inference models allows us to account for confounders such as diet, socioeconomic status, and pre-existing health conditions, offering a more comprehensive understanding of the relationship between physical activity and diabetes prevention. This study contributes to the growing literature by demonstrating that physical activity significantly reduces diabetes risk, with particular benefits for high-risk subgroups. Our findings provide evidence for public health policies that emphasize physical activity as a cornerstone of diabetes prevention, promoting individualized approaches to intervention.
Deep Learning CNN for Pneumonia Detection: Advancing Digital Health in Society 5.0 Almohab, Hadi
Jurnal Ilmiah Profesi Pendidikan Vol. 10 No. 4 (2025): November
Publisher : Fakultas Keguruan dan Ilmu Pendidikan, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jipp.v10i4.4001

Abstract

Pneumonia merupakan masalah kesehatan global yang serius dan menyumbang tingkat morbiditas serta mortalitas yang tinggi, terutama di wilayah dengan keterbatasan alat diagnostik dan sumber daya kesehatan. Penelitian ini bertujuan mengembangkan model Convolutional Neural Network (CNN) berbasis deep learning untuk mendeteksi pneumonia secara otomatis menggunakan citra X-ray dada. Metode yang digunakan meliputi pelatihan model pada dataset berlabel dengan serangkaian teknik pra-pemrosesan, seperti normalisasi, augmentasi data, dan peningkatan kualitas citra untuk memperbaiki ketahanan dan kemampuan generalisasi model. Hasil pengujian menunjukkan bahwa model yang dioptimalkan mencapai akurasi uji 91,67%, dengan nilai ROC-AUC 0,96 dan PR-AUC 0,95, yang menandakan performa kuat dalam membedakan pneumonia dari citra normal. Kesimpulannya, model CNN ini memiliki potensi signifikan sebagai alat bantu diagnostik yang cepat, konsisten, dan andal, serta mendukung visi Society 5.0 dalam integrasi kecerdasan buatan untuk meningkatkan layanan kesehatan dan kesejahteraan masyarakat.
Co-Authors A A Abushammala, Haneen Deden Witarsyah