A Conceptual Framework for a Lightweight AI System for Skin Disease Risk Prediction Using Epidemiological Data in Rural Bangladesh
DOI:
https://doi.org/10.31436/ijpcc.v12i1.642Keywords:
Skin disease risk prediction, Epidemiology, Lightweight AI, Rural Healthcare, Machine learningAbstract
Skin disease remains a significant public health issue in rural Bangladesh, where limited access to dermatologists and inadequate diagnostic facilities often delay accurate assessment and treatment. To address these constraints, this conceptual paper presents a lightweight AI-based framework for predicting skin disease risks using structured epidemiological data gathered from hospital visits and interviews with patients and healthcare staff. The framework incorporates environmental, occupational, hygiene-related, and living-condition factors to model individual risk profiles. Preliminary experiments conducted on an existing dataset demonstrate that conventional machine learning algorithms, particularly K-Nearest Neighbors (KNN) and Random Forest, achieve strong predictive performance, with accuracy reaching up to 88% in train–test evaluations and 80% in 10-fold cross-validation. These results confirm the viability of achieving high diagnostic reliability without image-based tools, relying solely on patient and environmental attributes. The findings further support the practical feasibility of deploying the proposed model in resource-limited rural clinics to aid early risk identification and more efficient allocation of healthcare resources. Privacy protection is incorporated as a core component to ensure secure and ethical handling of patient information.
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