Stroke Prediction Based on Machine Learning
Stroke has become an important cause of death and disability worldwide, which highlights the need for early detection and intervention. Machine learning technology can analyze patients’ historical health data and biometrics to identify high-risk individuals in a timely manner, thereby effectively pr...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04029.pdf |
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| _version_ | 1825206563934044160 |
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| author | Zhang Yuhan |
| author_facet | Zhang Yuhan |
| author_sort | Zhang Yuhan |
| collection | DOAJ |
| description | Stroke has become an important cause of death and disability worldwide, which highlights the need for early detection and intervention. Machine learning technology can analyze patients’ historical health data and biometrics to identify high-risk individuals in a timely manner, thereby effectively predicting stroke.This paper evaluates the predictive performance Random Forest and Support Vector Machine (SVM). Data preprocessing encompasses managing missing data, processing categorical variables, and tackling issues related to class imbalance. Analysis of the quantitative results indicates that the Random Forest model reaches an accuracy of 95% and a precision of 93%, providing a slight edge over the SVM, which records an accuracy of 92% and a precision of 90%.. However, both models exhibit high false-negative rates, with Random Forest showing a false-negative rate of 12% and SVM at 15%, which significantly impacts their clinical utility. To improve performance, further model optimization, such as adjusting class weights or employing ensemble methods, is necessary to reduce these false-negative rates and enhance diagnostic accuracy. This study highlights the potential and limitations of machine learning in stroke prediction, showing that people need further optimization to enhance diagnostic performance. |
| format | Article |
| id | doaj-art-613c8f91828b4d05af54a11abf1a177e |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-613c8f91828b4d05af54a11abf1a177e2025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700402910.1051/itmconf/20257004029itmconf_dai2024_04029Stroke Prediction Based on Machine LearningZhang Yuhan0Marlan and Rosemary Bourns College of Engineering, University of CaliforniaStroke has become an important cause of death and disability worldwide, which highlights the need for early detection and intervention. Machine learning technology can analyze patients’ historical health data and biometrics to identify high-risk individuals in a timely manner, thereby effectively predicting stroke.This paper evaluates the predictive performance Random Forest and Support Vector Machine (SVM). Data preprocessing encompasses managing missing data, processing categorical variables, and tackling issues related to class imbalance. Analysis of the quantitative results indicates that the Random Forest model reaches an accuracy of 95% and a precision of 93%, providing a slight edge over the SVM, which records an accuracy of 92% and a precision of 90%.. However, both models exhibit high false-negative rates, with Random Forest showing a false-negative rate of 12% and SVM at 15%, which significantly impacts their clinical utility. To improve performance, further model optimization, such as adjusting class weights or employing ensemble methods, is necessary to reduce these false-negative rates and enhance diagnostic accuracy. This study highlights the potential and limitations of machine learning in stroke prediction, showing that people need further optimization to enhance diagnostic performance.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04029.pdf |
| spellingShingle | Zhang Yuhan Stroke Prediction Based on Machine Learning ITM Web of Conferences |
| title | Stroke Prediction Based on Machine Learning |
| title_full | Stroke Prediction Based on Machine Learning |
| title_fullStr | Stroke Prediction Based on Machine Learning |
| title_full_unstemmed | Stroke Prediction Based on Machine Learning |
| title_short | Stroke Prediction Based on Machine Learning |
| title_sort | stroke prediction based on machine learning |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04029.pdf |
| work_keys_str_mv | AT zhangyuhan strokepredictionbasedonmachinelearning |