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 resourcelimited 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
Islam et al. (Fri,) studied this question.