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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM43992
Volume:
9 |
4 |
Apr 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
â This system offers an advanced solution for stroke prediction by combining Artificial Intelligence (AI) and Natural Language Processing (NLP) to enhance early detection and prevention. It analyses structured health data, including medical records and wearable vitals, alongside unstructured data from clinical notes, patient reports, and online health forums. Advanced machine learning techniques such as Random Forest and Gradient Boosting are applied for accurate stroke risk prediction. NLP methods like Named Entity Recognition (NER) and sentiment analysis extract valuable insights from unstructured medical text. The model delivers personalized prevention strategies, including lifestyle changes, dietary recommendations, and early medical intervention. Real-time alert mechanisms are also integrated to detect warning signs and initiate emergency responses. This system helps prevent strokes by enabling timely risk detection, improving patient care, and supporting early, personalized treatment decisions using AI-driven insights.
KEYWORDS
Publication Date
4/7/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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