AI-Assisted Healthcare Kiosk: Offline Disease Prediction, Medicine Recommendation, and Telemedicine Access for Rural India

Abstract

Rural communities in India face ongoing challenges in accessing primary healthcare. These challenges include a lack of doctors, geographical isolation, and high consultation costs. This paper discusses an AI-assisted healthcare kiosk, a web-based, fully offline system that provides automated disease prediction, medication recommendations, BMI assessment, cardiovascular risk evaluation, and doctor referrals. It does all this without needing a permanent physician or internet connection. The system uses a Decision Tree classifier along with several machine learning models trained on a structured dataset of 4,920 samples, 132 symptom features, and 41 disease categories. It achieved 100% classification accuracy on the test set. The Flask-based web application features offline speech recognition through Kiosk, text-to-speech output using pyttsx3, TF-IDF vectorisation for natural language processing, and a MySQL backend for managing patient sessions. All ten functional test cases were successful, with an end-to-end response time of about one second. The proposed system proves it is possible to implement AI-powered primary healthcare tools in rural and resource-limited areas.