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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Brain stroke prediction using machine learning |
| Authors Name: | Abdul Muiz , Prof. Najumusher H , Daiwik N , Edwin K.B , Join George |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_202070
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| Published Paper Id: | IJRTI2504080 |
| Published In: | Volume 10 Issue 4, April-2025 |
| DOI: | |
| Abstract: | Machine Learning (ML) has been a vital resource in medical diagnosis of special concern in predicting and reacting to brain strokes. Early identification of stroke markers is central towards preventing severity and improving patients' recovery. This paper classifies the ML algorithms used in stroke prediction into four categories based on purpose. Systematic analysis of 39 studies from 2007-2019 found that the most employed model was Support Vector Machine (SVM) and most frequently used data was CT imaging. Nevertheless, even though most of the research has been targeted towards stroke diagnosis, fewer studies are carried out on strokes management and stroke treatment optimization.This research uses the Kaggle stroke prediction dataset to compare the performance of different ML algorithms, including K-Nearest Neighbor (KNN), Logistic Regression, Random Forest, XGBoost, and LightGBM. The models were tested based on Precision, Recall, and F1-Score measures. Experimental results indicate that LightGBM achieved the highest classification accuracy of 99%, which outperformed all other models. The research depicts the extent to which ML can be used to predict strokes, whereby it is observed in use in early intervention and decision-making in practice.Apart from this, the study discovers that applying ML in medicine is able to quantify the risk of a stroke effectively and assist in enhancing the treatment procedures. The study also demands more effort towards ML technologies for the treatment of strokes, including refining the treatment protocols and personalized therapeutic advice. The future study needs to embrace the application of combined deep learning models with real-world patient data in order to be more efficient and precise in predicting strokes.Lastly, ML methods can detect and predict strokes in the brain. By implementing intricate algorithms, physicians are able to improve diagnostic accuracy, improve the success rate of treatment, and eventually decrease mortality and morbidity due to strokes. This study speaks volumes about possibilities in utilizing ML approaches in medicine, providing the way towards better and smart healthcare interventions. |
| Keywords: | Computer learning, brain injury. Ischemic stroke and transient ischemia attack both occur. |
| Cite Article: | "Brain stroke prediction using machine learning ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.a598-a609, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504080.pdf |
| Downloads: | 000364 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2504080
Registration ID:202070
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier):
Page No: a598-a609 Country: Bengaluru , Karnataka , India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504080 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504080 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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