Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 17, 04 December 2023
Open
Access | Article
Predicting the risk of stroke based on machine learning
* Author to whom correspondence should be addressed.
Abstract
Stroke, the second largest leading cause of death among all chronic diseases, is affecting about 101 million people in the world currently. It is estimated that this number of stroke cases will increase by 2.25 by the year 2050. Considering the large number of potential patients with stroke, a mathematical model is designed to predict one’s risk of having a stroke in the future based on one’s basic health data using machinery methods. Using the algorithm of Logistic Regression, the model reaches an accuracy of 92.28% when predicting whether one has a stroke, the model also validates that hypertension is the leading cause of the incidence of stroke by finding out the highest correlation value among all the feature variables. People who would like to know their probability of having a stroke can use the model, then they can have some precautionary measures to lower the likelihood of happening of stroke based on the prediction given, which helps save the medical costs and overuse of medical resources. Governments can enact policies and allocate medical resources based on the predictions made by the model.
Keywords
Stroke, Prediction, Probability
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Modern Medicine and Global Health
- ISBN (Print)
- 978-1-83558-197-1
- ISBN (Online)
- 978-1-83558-198-8
- Published Date
- 04 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/17/20240623
- Copyright
- 04 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited