International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Early Stroke Risk Detection using ML Models |
|---|---|
| ABSTRACT | A stroke is extremely serious and needs fast medical treatment. If care is delayed, it can cause long-term disability or even death. Ischemic strokes, in particular, must be treated within a few hours for the best chance of recovery. A mini stroke, or TIA (Transient Ischemic Attack), causes temporary symptoms that usually disappear within 24 hours, but it is still an emergency because it can be a warning sign of a future major stroke. The World Health Organization reports that stroke is the third biggest cause of death globally, accounting for about 10.7% of all deaths. In our project, we use machine learning to estimate a person’s chances of having a stroke. The model studies important health and lifestyle factors such as age, gender, blood pressure, glucose levels, marital status, and smoking habits to predict stroke risk. |
| AUTHOR | DR. S. P. KHANDAIT, BHUVNESHWARI JANBANDHU, MEGHANA PARATE, MRUNALI BARSAGADE, NANDINI PESHNE Head of Department & Project Guide, Dept. of Information Technology, KDK College of Engineering, Nagpur, Maharashtra, India Dept. of Information Technology, KDK College of Engineering, Nagpur, Maharashtra, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312021 |
| pdf/21_Early Stroke Risk Detection using ML Models.pdf | |
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