Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction
Many epidemics have afflicted humanity throughout history, claiming many lives. It has been noted in our time that heart disease is one of the deadliest diseases that humanity has confronted in the contemporary period. The proliferation of poor habits such as smoking, overeating, and lack of physica...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/12/3215 |
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| author | Ahmed Almulihi Hager Saleh Ali Mohamed Hussien Sherif Mostafa Shaker El-Sappagh Khaled Alnowaiser Abdelmgeid A. Ali Moatamad Refaat Hassan |
| author_facet | Ahmed Almulihi Hager Saleh Ali Mohamed Hussien Sherif Mostafa Shaker El-Sappagh Khaled Alnowaiser Abdelmgeid A. Ali Moatamad Refaat Hassan |
| author_sort | Ahmed Almulihi |
| collection | DOAJ |
| description | Many epidemics have afflicted humanity throughout history, claiming many lives. It has been noted in our time that heart disease is one of the deadliest diseases that humanity has confronted in the contemporary period. The proliferation of poor habits such as smoking, overeating, and lack of physical activity has contributed to the rise in heart disease. The killing feature of heart disease, which has earned it the moniker the “silent killer,” is that it frequently has no apparent signs in advance. As a result, research is required to develop a promising model for the early identification of heart disease using simple data and symptoms. The paper’s aim is to propose a deep stacking ensemble model to enhance the performance of the prediction of heart disease. The proposed ensemble model integrates two optimized and pre-trained hybrid deep learning models with the Support Vector Machine (SVM) as the meta-learner model. The first hybrid model is Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) (CNN-LSTM), which integrates CNN and LSTM. The second hybrid model is CNN-GRU, which integrates CNN with a Gated Recurrent Unit (GRU). Recursive Feature Elimination (RFE) is also used for the feature selection optimization process. The proposed model has been optimized and tested using two different heart disease datasets. The proposed ensemble is compared with five machine learning models including Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (K-NN), Decision Tree (DT), Naïve Bayes (NB), and hybrid models. In addition, optimization techniques are used to optimize ML, DL, and the proposed models. The results obtained by the proposed model achieved the highest performance using the full feature set. |
| first_indexed | 2024-03-09T17:08:05Z |
| format | Article |
| id | doaj.art-1beee78246ce4260b7e8ceaa3ae0f652 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T17:08:05Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-1beee78246ce4260b7e8ceaa3ae0f6522023-11-24T14:20:45ZengMDPI AGDiagnostics2075-44182022-12-011212321510.3390/diagnostics12123215Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early PredictionAhmed Almulihi0Hager Saleh1Ali Mohamed Hussien2Sherif Mostafa3Shaker El-Sappagh4Khaled Alnowaiser5Abdelmgeid A. Ali6Moatamad Refaat Hassan7Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaFaculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, EgyptDepartment of Computer Science, Faculty of Science, Aswan University, Aswan 81528, EgyptFaculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, EgyptFaculty of Computer Science and Engineering, Galala University, Suez 34511, EgyptCollege of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi ArabiaFaculty of Computers and Information, Minia University, Minia 61519, EgyptDepartment of Computer Science, Faculty of Science, Aswan University, Aswan 81528, EgyptMany epidemics have afflicted humanity throughout history, claiming many lives. It has been noted in our time that heart disease is one of the deadliest diseases that humanity has confronted in the contemporary period. The proliferation of poor habits such as smoking, overeating, and lack of physical activity has contributed to the rise in heart disease. The killing feature of heart disease, which has earned it the moniker the “silent killer,” is that it frequently has no apparent signs in advance. As a result, research is required to develop a promising model for the early identification of heart disease using simple data and symptoms. The paper’s aim is to propose a deep stacking ensemble model to enhance the performance of the prediction of heart disease. The proposed ensemble model integrates two optimized and pre-trained hybrid deep learning models with the Support Vector Machine (SVM) as the meta-learner model. The first hybrid model is Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) (CNN-LSTM), which integrates CNN and LSTM. The second hybrid model is CNN-GRU, which integrates CNN with a Gated Recurrent Unit (GRU). Recursive Feature Elimination (RFE) is also used for the feature selection optimization process. The proposed model has been optimized and tested using two different heart disease datasets. The proposed ensemble is compared with five machine learning models including Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (K-NN), Decision Tree (DT), Naïve Bayes (NB), and hybrid models. In addition, optimization techniques are used to optimize ML, DL, and the proposed models. The results obtained by the proposed model achieved the highest performance using the full feature set.https://www.mdpi.com/2075-4418/12/12/3215machine learningdeep learningensemble learningheart disease |
| spellingShingle | Ahmed Almulihi Hager Saleh Ali Mohamed Hussien Sherif Mostafa Shaker El-Sappagh Khaled Alnowaiser Abdelmgeid A. Ali Moatamad Refaat Hassan Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction Diagnostics machine learning deep learning ensemble learning heart disease |
| title | Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction |
| title_full | Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction |
| title_fullStr | Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction |
| title_full_unstemmed | Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction |
| title_short | Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction |
| title_sort | ensemble learning based on hybrid deep learning model for heart disease early prediction |
| topic | machine learning deep learning ensemble learning heart disease |
| url | https://www.mdpi.com/2075-4418/12/12/3215 |
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