Stroke risk prediction: a deep learning approach for identifying high-risk patients
Abstract The application of Artificial Intelligence (AI) to diverse field has been widely accepted ranging from transportation, education, logistics, entertainment and health. Specifically, in recent time, the application of Machine Learning (ML) a subset of AI has equally got wide acceptance and re...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Data |
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| Online Access: | https://doi.org/10.1007/s44248-025-00070-2 |
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| author | Afeez A. Soladoye Kazeem M. Olagunju Sunday A. Ajagbe Ibrahim A. Adeyanju Precious I. Ogie Pragasen Mudali |
| author_facet | Afeez A. Soladoye Kazeem M. Olagunju Sunday A. Ajagbe Ibrahim A. Adeyanju Precious I. Ogie Pragasen Mudali |
| author_sort | Afeez A. Soladoye |
| collection | DOAJ |
| description | Abstract The application of Artificial Intelligence (AI) to diverse field has been widely accepted ranging from transportation, education, logistics, entertainment and health. Specifically, in recent time, the application of Machine Learning (ML) a subset of AI has equally got wide acceptance and relevance in various aspect of medicine ranging from diagnosis and prediction of diseases, development of drugs and treatment plan among others. This has made medical procedure to be faster, more accurate and easier than using the traditional approach. Stroke is reported to be one of the major causes of death and this can be reduced by studying the risk factors causing it and predicting its occurrence so as to educate people about it. This study developed a stroke prediction system with a modified Gated Recurrent Unit (GRU), a structured stroke dataset was gotten from Kaggle, which went through different preprocessing techniques like label Encoder, Normalization with MinMax, dropping of irrelevant values. Furthermore, different data balancing techniques were employed to improve the accurate performance of the model. The preprocessed dataset was used by GRU for prediction. The system gave average accuracy, Area Under Curve (AUC) and prediction time of 80.42%, 0.8940 and 0.678 s respectively. The developed system outperformed other ML algorithms like LSTM, GRU-LSTM, Support Vector Machine (SVM) and Logistic Regression. This study showed that GRU a variant of Recurrent Neural Network, could give better predictive performance on structured data rather than only with streaming data; therefore, showcasing the improved performance of GRU over other variants of RNN. |
| format | Article |
| id | doaj-art-b2b20cb247e54daaaa406e82f7b29ce4 |
| institution | Kabale University |
| issn | 2731-6955 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Data |
| spelling | doaj-art-b2b20cb247e54daaaa406e82f7b29ce42025-08-20T03:46:29ZengSpringerDiscover Data2731-69552025-07-013111910.1007/s44248-025-00070-2Stroke risk prediction: a deep learning approach for identifying high-risk patientsAfeez A. Soladoye0Kazeem M. Olagunju1Sunday A. Ajagbe2Ibrahim A. Adeyanju3Precious I. Ogie4Pragasen Mudali5Department of Computer Engineering, Federal UniversityDepartement of Computer Science, Landmark UniversityDepartment of Computer Engineering, Abiola Ajimobi Technical UniversityDepartment of Computer Engineering, Federal UniversityUniversity of Buckingham Medical SchoolDepartment of Computer Engineering, Abiola Ajimobi Technical UniversityAbstract The application of Artificial Intelligence (AI) to diverse field has been widely accepted ranging from transportation, education, logistics, entertainment and health. Specifically, in recent time, the application of Machine Learning (ML) a subset of AI has equally got wide acceptance and relevance in various aspect of medicine ranging from diagnosis and prediction of diseases, development of drugs and treatment plan among others. This has made medical procedure to be faster, more accurate and easier than using the traditional approach. Stroke is reported to be one of the major causes of death and this can be reduced by studying the risk factors causing it and predicting its occurrence so as to educate people about it. This study developed a stroke prediction system with a modified Gated Recurrent Unit (GRU), a structured stroke dataset was gotten from Kaggle, which went through different preprocessing techniques like label Encoder, Normalization with MinMax, dropping of irrelevant values. Furthermore, different data balancing techniques were employed to improve the accurate performance of the model. The preprocessed dataset was used by GRU for prediction. The system gave average accuracy, Area Under Curve (AUC) and prediction time of 80.42%, 0.8940 and 0.678 s respectively. The developed system outperformed other ML algorithms like LSTM, GRU-LSTM, Support Vector Machine (SVM) and Logistic Regression. This study showed that GRU a variant of Recurrent Neural Network, could give better predictive performance on structured data rather than only with streaming data; therefore, showcasing the improved performance of GRU over other variants of RNN.https://doi.org/10.1007/s44248-025-00070-2Gated recurrent unitsStrokePredictionData balancing techniquesMachine learning |
| spellingShingle | Afeez A. Soladoye Kazeem M. Olagunju Sunday A. Ajagbe Ibrahim A. Adeyanju Precious I. Ogie Pragasen Mudali Stroke risk prediction: a deep learning approach for identifying high-risk patients Discover Data Gated recurrent units Stroke Prediction Data balancing techniques Machine learning |
| title | Stroke risk prediction: a deep learning approach for identifying high-risk patients |
| title_full | Stroke risk prediction: a deep learning approach for identifying high-risk patients |
| title_fullStr | Stroke risk prediction: a deep learning approach for identifying high-risk patients |
| title_full_unstemmed | Stroke risk prediction: a deep learning approach for identifying high-risk patients |
| title_short | Stroke risk prediction: a deep learning approach for identifying high-risk patients |
| title_sort | stroke risk prediction a deep learning approach for identifying high risk patients |
| topic | Gated recurrent units Stroke Prediction Data balancing techniques Machine learning |
| url | https://doi.org/10.1007/s44248-025-00070-2 |
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