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Published Paper Details:
Next-Gen AI Powered Stroke Risk Prediction System using Deep Neural Networks
Authors
Mohammed Aadil A,Mohammed Saqib A,Najebullah N,Syed Shabeer,Nishanthi M
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Keywords
Stroke RisStroke Risk Prediction, Deep Neural Networks (DNN), Artificial Intelligence (AI) in Healthcare, Predictive Analytics, Medical Diagnosis System, Health Risk Assessment, Machine Learning in Medicine, Clinical Decision Support System, Flask Web Application, Biomedical Data Analysis, Real-time Risk Prediction, AI-Powered Health Monitoring, Smart Healthcare System, Personalized Medical Recommendations, Explainable AI (XAI)k Prediction Deep Neural Networks (DNN) Artificial Intelligence (AI
Abstract
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
IJCRT's Publication Details
Unique Identification Number - IJCRT2505050
Paper ID - 284961
Author type - Indian Author
Page Number(s) - a428-a433
Pubished in - Volume 13 | Issue 5 | May 2025
DOI (Digital Object Identifier) -
No Of Downloads - 124
Author Country - India, 600 055, Chennai, Chennai, 600 055, Humanities All
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Mohammed Aadil A,Mohammed Saqib A,Najebullah N,Syed Shabeer,Nishanthi M, "Next-Gen AI Powered Stroke Risk Prediction System using Deep Neural Networks", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 5, pp.a428-a433, May 2025, Available at :http://www.ijcrt.org/papers/IJCRT2505050.pdf
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