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All Journal Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Jurnal Promkes: The Indonesian Journal of Health Promotion and Health Education Jurnal Ilmu Kesehatan Bhakti Husada: Health Sciences Journal Journal La Sociale Jurnal Manajemen Pendidikan Al Hadi Carmin: Journal of Community Service Jurnal Kesehatan Reproduksi Holistik Jurnal Kesehatan
Erlena, Erlena
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Agriculture, Biological Sciences & Forestry Humanities Earth & Planetary Sciences Education Health Professions Nursing Public Health Social Sciences Other

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Salutogenic Approach to Early Kidney Health Promotion: Comparing Deep Learning Facial Analysis and Questionnaire Screening in Adolescents Nurjannah, Uun; erlena, Erlena; wahyudi; Suryana, Iham
Jurnal Promkes: The Indonesian Journal of Health Promotion and Health Education Vol. 14 No. SI1 (2026): Jurnal Promkes: The Indonesian Journal of Health Promotion and Health Educat
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jpk.V14.ISI1.2026.50-58

Abstract

Background: Chronic kidney disease (CKD) is increasingly reported in adolescents and is driven by modifiable lifestyle factors such as inadequate hydration, frequent consumption of processed foods, obesity, and poor sleep quality. Because early CKD is typically asymptomatic, routine laboratory screening is often impractical in school or community settings. Self-reported questionnaires can help estimate lifestyle-related risk but rely on subjective recall. Objective: To compare the effectiveness of a DL-based facial analysis model with self-reported questionnaire screening for early CKD risk in adolescents. Methods: A cross-sectional study was conducted among 100 adolescents aged 16–19 in Karawang, Indonesia. Participants completed validated questionnaires assessing hydration, dietary habits, and sleep quality using the Pittsburgh Sleep Quality Index (PSQI). Standardized facial photographs were analyzed using a convolutional neural network (CNN) trained to detect CKD-related facial markers. Agreement between methods was assessed using Cohen’s kappa, and diagnostic performance was evaluated using ROC analysis. Results: Questionnaire screening classified 88% of adolescents as low-risk, 10% moderate-risk, and 2% high-risk, while the AI model classified 95%, 4%, and 1%, respectively, demonstrating moderate agreement (κ = 0.61, p < 0.001). The AI model achieved 91.0% accuracy, 88.0% sensitivity, 92.0% specificity, and an AUC of 0.904. Overweight adolescents had higher odds of being at risk (OR = 2.35). Conclusion: Combining AI-based facial analysis with questionnaire assessment provides a rapid, scalable, and non-invasive strategy for early CKD risk screening in adolescents, particularly in resource-limited settings.
Co-Authors Alfiansyah, Deden Moh Edi Setiadi Eko Suhartono Farida, Nita Hafifah, Ifa Helana, Ricky Henny Lilyanti, Henny Iskandar Iskandar Isna Syauqiah Nita Syamsiah, Nita Nurdiansyah, Tubagus Erwin Nurhasanah Nurhasanah Nurjanah, Uun Nurjannah, Uun Priambodo, Anton Putera, Gusti Muhammad Perdana Ratna Januarita sari, nina yuliana Sekartaji, Hapsari Lintang Sopiatun, Siti Sudiono Sudiono, Sudiono Suryana, Iham Trang, Ha Thi Thu WAHYUDI