Stroke Prediction Using Deep Learning and Transfer Learning Approaches
Stroke is one of the leading causes of death and disability worldwide. The ideal solution to the stroke problem is to prevent it in advance by controlling metabolic factors, atrial fibrillation, hypertension, smoking, Etc. However, unless the physiological indicators are abnormal, it is difficult fo...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10599507/ |
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| author | Dong-Her Shih Yi-Huei Wu Ting-Wei Wu Huei-Ying Chu Ming-Hung Shih |
| author_facet | Dong-Her Shih Yi-Huei Wu Ting-Wei Wu Huei-Ying Chu Ming-Hung Shih |
| author_sort | Dong-Her Shih |
| collection | DOAJ |
| description | Stroke is one of the leading causes of death and disability worldwide. The ideal solution to the stroke problem is to prevent it in advance by controlling metabolic factors, atrial fibrillation, hypertension, smoking, Etc. However, unless the physiological indicators are abnormal, it is difficult for medical personnel to decide whether special precautions are necessary for a patient based solely on monitoring the potential patient. There was a great category imbalance between stroke and non-stroke patients, so this study tried to use various techniques to solve the problem of categorical unbalanced stroke prediction problem. Then, deep learning models were used to predict whether the patients would have a stroke. Finally, the classification experiment is carried out through transfer learning to observe whether the evaluation metrics are further improved. According to the experimental results, this study effectively reduced the false negative rate (FNR) and false positive rate (FPR) of stroke prediction and improved the overall accuracy of stroke prediction through the category imbalance treatment and deep learning method. |
| format | Article |
| id | doaj-art-c68cd1aa73604adc8d1b6a2308e819fe |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c68cd1aa73604adc8d1b6a2308e819fe2025-08-20T01:54:57ZengIEEEIEEE Access2169-35362024-01-011213009113010410.1109/ACCESS.2024.342915710599507Stroke Prediction Using Deep Learning and Transfer Learning ApproachesDong-Her Shih0https://orcid.org/0000-0003-1605-8488Yi-Huei Wu1https://orcid.org/0009-0003-1839-0086Ting-Wei Wu2https://orcid.org/0000-0002-9391-1852Huei-Ying Chu3Ming-Hung Shih4https://orcid.org/0000-0002-8745-5109Department of Information Management, National Yunlin University of Science and Technology, Douliu, Yunlin, TaiwanDepartment of Information Management, National Yunlin University of Science and Technology, Douliu, Yunlin, TaiwanDepartment of Information Management, National Yunlin University of Science and Technology, Douliu, Yunlin, TaiwanDepartment of Information Management, National Yunlin University of Science and Technology, Douliu, Yunlin, TaiwanDepartment of Electrical and Computer Engineering, Iowa State University, Ames, IA, USAStroke is one of the leading causes of death and disability worldwide. The ideal solution to the stroke problem is to prevent it in advance by controlling metabolic factors, atrial fibrillation, hypertension, smoking, Etc. However, unless the physiological indicators are abnormal, it is difficult for medical personnel to decide whether special precautions are necessary for a patient based solely on monitoring the potential patient. There was a great category imbalance between stroke and non-stroke patients, so this study tried to use various techniques to solve the problem of categorical unbalanced stroke prediction problem. Then, deep learning models were used to predict whether the patients would have a stroke. Finally, the classification experiment is carried out through transfer learning to observe whether the evaluation metrics are further improved. According to the experimental results, this study effectively reduced the false negative rate (FNR) and false positive rate (FPR) of stroke prediction and improved the overall accuracy of stroke prediction through the category imbalance treatment and deep learning method.https://ieeexplore.ieee.org/document/10599507/Machine learningdeep learningtransfer learningstroke prediction |
| spellingShingle | Dong-Her Shih Yi-Huei Wu Ting-Wei Wu Huei-Ying Chu Ming-Hung Shih Stroke Prediction Using Deep Learning and Transfer Learning Approaches IEEE Access Machine learning deep learning transfer learning stroke prediction |
| title | Stroke Prediction Using Deep Learning and Transfer Learning Approaches |
| title_full | Stroke Prediction Using Deep Learning and Transfer Learning Approaches |
| title_fullStr | Stroke Prediction Using Deep Learning and Transfer Learning Approaches |
| title_full_unstemmed | Stroke Prediction Using Deep Learning and Transfer Learning Approaches |
| title_short | Stroke Prediction Using Deep Learning and Transfer Learning Approaches |
| title_sort | stroke prediction using deep learning and transfer learning approaches |
| topic | Machine learning deep learning transfer learning stroke prediction |
| url | https://ieeexplore.ieee.org/document/10599507/ |
| work_keys_str_mv | AT donghershih strokepredictionusingdeeplearningandtransferlearningapproaches AT yihueiwu strokepredictionusingdeeplearningandtransferlearningapproaches AT tingweiwu strokepredictionusingdeeplearningandtransferlearningapproaches AT hueiyingchu strokepredictionusingdeeplearningandtransferlearningapproaches AT minghungshih strokepredictionusingdeeplearningandtransferlearningapproaches |