Community healthcare systems often struggle with delayed disease identification, limited interaction between healthcare professionals and citizens, and the absence of real-time local health insights; these challenges result in late medical intervention and increased disease spread, especially at the ward and street level. This paper presents a community-centric health intelligence system for disease monitoring and awareness, a web-based, location- aware platform that leverages Natural Language Processing (NLP) to analyse user-reported symptoms and detect early disease patterns within small geographical communities. The proposed system groups users based on residential wards, processes unstructured text-based symptom data, and applies threshold-based analytics to classify health conditions as normal, awareness needed, or medical camp required. A doctor awareness module enables healthcare professionals to share educational content, schedule medical camps, and monitor community health through an interactive dashboard. The system operates without additional hardware, relies solely on user participation and cloud infrastructure, and aims to strengthen preventive healthcare by enabling early intervention and community-wide awareness. This approach contributes to Sustainable Development Goals (SDG 3: Good Health and Well-being), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 11 (Sustainable Cities and Communities).
Revathi et al. (Thu,) studied this question.