SEHAT (Smart E-Healthcare Assistant & Tracker)

Abstract

The rapid advancement of Artificial Intelligence (AI) has created new opportunities for enhancing early disease detection, healthcare accessibility, and personalized medical assistance. However, existing e-health applications typically operate as isolated systems, offering limited diagnostic scope, minimal contextual understanding, or single-modality analysis. To address these limitations, SEHAT (Smart E-Healthcare Assistant & Tracker) is proposed as a multi-modal AI platform that integrates symptom-based prediction, radiological image analysis, and conversational medical support into a unified digital ecosystem. The system employs an ensemble of machine learning algorithms for text-based symptom classification and a convolutional neural network (CNN) for chest X-ray abnormality detection, achieving significant accuracy in identifying conditions such as pneumonia and tuberculosis. Additionally, a generative-AI-powered conversational module enables context-aware interactions for patient guidance and health literacy. Implemented using Python, Flask, and Streamlit, SEHAT addresses key challenges of fragmented healthcare systems by offering an accessible, explainable, and user-friendly decision-support tool. Designed strictly as a preliminary screening assistant—not a substitute for clinical diagnosis—SEHAT demonstrates strong potential to enhance early detection, reduce diagnostic delays, and support underserved populations through intelligent digital healthcare.

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