ARTIFICIAL INTELLIGENCE-ASSISTED SCREENING FOR NON-COMMUNICABLE DISEASE RISK IN COMMUNITY-BASED PRIMARY HEALTHCARE

Kusumayadi; Rian Saprial; Marnah

doi:10.55681/aohj.v3i1.559

Authors

Kusumayadi Institut Teknologi dan Kesehatan Aspirasi
Rian Saprial Institut Teknologi dan Kesehatan Aspirasi
Marnah Institut Teknologi dan Kesehatan Aspirasi

DOI:

https://doi.org/10.55681/aohj.v3i1.559

Keywords:

ARTIFICIAL INTELLIGENCE, PRIMARY HEALTHCARE, ARTIFICIAL INTELLIGENCE-ASSISTED SCREENING

Abstract

Non-communicable diseases remain a major challenge for primary healthcare because hypertension, diabetes, cardiovascular disease, and chronic kidney disease frequently develop silently before clinical diagnosis. This study evaluated an artificial intelligence-assisted screening framework designed to identify adults at elevated non-communicable disease risk during community-based primary healthcare encounters. A quantitative diagnostic-prediction study was designed using a simulated dataset representing 1,200 adults attending community screening services. Demographic characteristics, blood pressure, body mass index, smoking status, physical activity, family history, random blood glucose, and selected routine clinical variables were used to compare logistic regression, random forest, and gradient boosting models. Model performance was assessed through discrimination, sensitivity, specificity, F1 score, calibration, and decision-oriented risk stratification. Gradient boosting produced the strongest overall discrimination, with an area under the receiver operating characteristic curve of 0.89, sensitivity of 0.84, and specificity of 0.81 for identifying participants classified as high risk. Age, systolic blood pressure, body mass index, glucose level, smoking, and family history contributed most strongly to predictions. AI-assisted screening also increased the proportion of high-risk individuals prioritized for confirmatory assessment compared with rule-based screening alone. The findings indicate that transparent artificial intelligence can strengthen early risk recognition in community primary care when it complements, rather than replaces, professional judgment. The proposed framework contributes a practical model for targeted prevention, referral prioritization, and more efficient use of limited primary healthcare resources while emphasizing local validation, fairness, explainability, privacy, and continuous performance monitoring. This.

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