Published Paper Details:

This paper presents an innovative healthcare technology solution that leverages artificial intelligence and machine learning to revolutionize stroke risk assessment and preventive healthcare management. The system implements a sophisticated deep neural network architecture trained on comprehensive patient health datasets to predict stroke risk with high accuracy. The model considers multiple critical health parameters including demographic factors (age, gender), physiological metrics (BMI, blood pressure), medical history (hypertension, heart disease), lifestyle indicators (smoking status, work type), and environmental factors (residence type). The implementation utilizes a modern tech stack comprising Flask (Python) for backend services, TensorFlow for AI/ML operations, and a responsive frontend built with Tailwind CSS. The system's architecture ensures secure data management through SQLite database integration and implements robust user authentication mechanisms. A key innovation is the dynamic recommendation engine that generates personalized health guidance based on individual risk profiles, incorporating age-specific advice, condition-based recommendations, and lifestyle modifications. The platform features a premium-grade user interface with dual-mode (light/dark) support, interactive dashboards for real-time analytics, and comprehensive PDF report generation with QR code integration for digital access. The reporting system provides detailed patient assessments, including risk stratification, health metrics visualization, and personalized preventive measures. Evaluation of the system demonstrates significant improvements in stroke risk prediction accuracy compared to traditional assessment methods. The implementation of personalized recommendations has shown promising results in patient engagement and preventive healthcare management. The system's modular architecture allows for continuous learning and improvement through ongoing data analysis and model refinement. This research contributes to the field of preventive healthcare by introducing an integrated solution that combines AI-driven risk assessment with personalized healthcare management. The system's ability to provide real-time risk analysis and tailored recommendations makes it a valuable tool for healthcare professionals and patients alike, potentially reducing stroke incidence through early intervention and preventive measures. Keywords: Stroke Risk Assessment, Artificial Intelligence, Deep Learning, Preventive Healthcare, Personalized Medicine, Health Analytics, Machine Learning, Medical Informatics