Abstract: This paper presents a fully integrated AI-Based Health Monitoring System that performs natural-language symptom analysis, machine learning classification, severity estimation, multilingual recommendation delivery, personal health record management, and real-time emergency support aligned with UN SDG-3. The system accepts free-text symptoms, processes them through TF-IDF vectorization and Logistic Regression, and combines predictive results with rule-based severity logic to ensure medically responsible triage. To improve accessibility, the system provides multilingual outputs in English, Hindi, and Kannada using an offline translation dictionary. Emergency response capabilities include geolocation-based hospital discovery using OpenStreetMap APIs, prioritizing hospitals with verified contact details. Implemented with Python Flask, scikit-learn, SQLite/PostgreSQL, HTML, CSS, and JavaScript, the platform provides safe and reliable triage with intentional over-triage to reduce false negatives in critical conditions.
Keywords: AI Healthcare, Symptom Analysis, Logistic Regression, Multilingual Health System, Emergency Support
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DOI:
10.17148/IJARCCE.2026.15121
[1] Jagadevi Puranikmath, Harsha D V, Hemanth K, Mohammed Fida Moinuddin J, Kiran A, "AI-Based Health Monitoring System," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2026.15121
