An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke
Early detection of stroke warning symptoms can help reduce the severity of ischemic stroke, the leading cause of mortality and disability worldwide. This study aims to develop a model to predict the disease by leveraging machine learning-based models. A model that concatenates a convolutional neural...
| Main Authors: | , , , , , , |
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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/10494753/ |
| _version_ | 1797197355213127680 |
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| author | Sapiah Sakri Shakila Basheer Zuhaira Muhammad Zain Nurul Halimatul Asmak Ismail Dua' Abdellatef Nassar Ghadah Nasser Aldehim Mais Ayman Alharaki |
| author_facet | Sapiah Sakri Shakila Basheer Zuhaira Muhammad Zain Nurul Halimatul Asmak Ismail Dua' Abdellatef Nassar Ghadah Nasser Aldehim Mais Ayman Alharaki |
| author_sort | Sapiah Sakri |
| collection | DOAJ |
| description | Early detection of stroke warning symptoms can help reduce the severity of ischemic stroke, the leading cause of mortality and disability worldwide. This study aims to develop a model to predict the disease by leveraging machine learning-based models. A model that concatenates a convolutional neural network and a long short-term memory was developed as the proposed model. Seven other classifiers were treated as the baseline models: logistic regression, random forest, extreme gradient boosting, k-nearest neighbor, artificial neural network, long short-term memory, and convolutional neural network. All models were trained using a healthcare dataset of 5110 patients’ health profiles. A synthetic minority oversampling technique was deployed to balance the data. Metrics such as accuracy, precision, F1-score, recall, area under the curve, and confusion metrics were used to evaluate the models’ performance. With a 95.9% accuracy, the proposed model outperformed the models employed in this study and improved the accuracy of prior studies that used the same dataset. The Shapley Additive Explanations method was applied to explain the result obtained by the best model. The proposed model was created to predict ischemic stroke. It considers each patient’s profile, allowing for personalized decision-making in resource-constrained settings. |
| first_indexed | 2024-04-24T06:42:39Z |
| format | Article |
| id | doaj.art-d5b667c1d7ab4b7b80fcb8200047f60c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T06:42:39Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d5b667c1d7ab4b7b80fcb8200047f60c2024-04-22T23:00:34ZengIEEEIEEE Access2169-35362024-01-0112531895320410.1109/ACCESS.2024.338622010494753An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic StrokeSapiah Sakri0https://orcid.org/0000-0001-5034-2752Shakila Basheer1https://orcid.org/0000-0001-9032-9560Zuhaira Muhammad Zain2https://orcid.org/0000-0002-5973-387XNurul Halimatul Asmak Ismail3https://orcid.org/0000-0002-2222-5644Dua' Abdellatef Nassar4Ghadah Nasser Aldehim5https://orcid.org/0000-0002-5511-8909Mais Ayman Alharaki6https://orcid.org/0000-0002-4793-8544Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science and Information Technology, Applied College, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaEarly detection of stroke warning symptoms can help reduce the severity of ischemic stroke, the leading cause of mortality and disability worldwide. This study aims to develop a model to predict the disease by leveraging machine learning-based models. A model that concatenates a convolutional neural network and a long short-term memory was developed as the proposed model. Seven other classifiers were treated as the baseline models: logistic regression, random forest, extreme gradient boosting, k-nearest neighbor, artificial neural network, long short-term memory, and convolutional neural network. All models were trained using a healthcare dataset of 5110 patients’ health profiles. A synthetic minority oversampling technique was deployed to balance the data. Metrics such as accuracy, precision, F1-score, recall, area under the curve, and confusion metrics were used to evaluate the models’ performance. With a 95.9% accuracy, the proposed model outperformed the models employed in this study and improved the accuracy of prior studies that used the same dataset. The Shapley Additive Explanations method was applied to explain the result obtained by the best model. The proposed model was created to predict ischemic stroke. It considers each patient’s profile, allowing for personalized decision-making in resource-constrained settings.https://ieeexplore.ieee.org/document/10494753/Ischemic stroke predictionSHAP methodhybrid deep learning modelmachine learning |
| spellingShingle | Sapiah Sakri Shakila Basheer Zuhaira Muhammad Zain Nurul Halimatul Asmak Ismail Dua' Abdellatef Nassar Ghadah Nasser Aldehim Mais Ayman Alharaki An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke IEEE Access Ischemic stroke prediction SHAP method hybrid deep learning model machine learning |
| title | An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke |
| title_full | An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke |
| title_fullStr | An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke |
| title_full_unstemmed | An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke |
| title_short | An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke |
| title_sort | improved concatenation of deep learning models for predicting and interpreting ischemic stroke |
| topic | Ischemic stroke prediction SHAP method hybrid deep learning model machine learning |
| url | https://ieeexplore.ieee.org/document/10494753/ |
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