AI-Based Real-Time Heart Stroke Prediction System with Chatbot Integration
Keywords:
Artificial Intelligence, Chatbot, Deep learning, Healthcare monitoring, IoT, Machine learning, Preventive healthcare, Stroke prediction, Wearable sensorsAbstract
Stroke is a life-threatening medical emergency that requires early detection to prevent permanent disability or death. Traditional healthcare systems rely on periodic hospital visits, limiting real-time monitoring for high-risk individuals. This paper proposes an intelligent, real-time heart stroke prediction system integrated with an AI-driven health chatbot. The system continuously collects physiological data from wearable sensors—including heart rate, blood pressure, and SpO₂ levels—along with clinical and lifestyle inputs such as age, BMI, hypertension history, smoking status, and glucose level. A trained deep learning model classifies stroke risk into Low, Medium, or High categories. High-risk cases trigger emergency notifications and email alerts. The integrated AI chatbot interprets prediction results, provides medically relevant explanations in simple language, and offers personalized lifestyle recommendations. A historical health dashboard and downloadable PDF reports further support preventive care. An administrative module enables dataset management and algorithm comparison using metrics such as accuracy, precision, recall, F1-score, and support. The proposed system functions as a proactive, user-friendly, intelligent decision-support platform that empowers individuals and healthcare providers with real-time stroke risk awareness and timely intervention capabilities.
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