Enhancing heart disease classification with M2MASC and CNN-BiLSTM integration for improved accuracy
Abstract Heart disease is a leading cause of death globally; therefore, accurate detection and classification are prominent, and several DL and ML methods have been developed over the last decade. However, the classical approaches may be prone to overfitting and under fitting issues, and the model p...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-74993-2 |
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| author | Vivek Pandey Umesh Kumar Lilhore Ranjan Walia Roobaea Alroobaea Majed Alsafyani Abdullah M. Baqasah Sultan Algarni |
| author_facet | Vivek Pandey Umesh Kumar Lilhore Ranjan Walia Roobaea Alroobaea Majed Alsafyani Abdullah M. Baqasah Sultan Algarni |
| author_sort | Vivek Pandey |
| collection | DOAJ |
| description | Abstract Heart disease is a leading cause of death globally; therefore, accurate detection and classification are prominent, and several DL and ML methods have been developed over the last decade. However, the classical approaches may be prone to overfitting and under fitting issues, and the model performance may lag due to the unavailability of annotated datasets. To overcome these issues, the research proposed a model for heart disease detection and classification by integrating blockchain technology with a Modified mixed attention-enabled search optimizer-based CNN-Bidirectional Long Short-Term Memory (M2MASC enabled CNN-BiLSTM) model. The novel model incorporates a pre-trained VGG16 model to enhance feature extraction and improve the overall predictive accuracy. Leveraging the continuous monitoring capabilities of IoT devices, patient data is collected in real-time, providing a dynamic source to the CNN-BiLSTM model. Blockchain integration ensures stored health data’s security, transparency, and immutability, addresses privacy concerns, and promotes trust in the predictive system. The classifier parameters are tuned using the modified mixed attention and search optimization. The M2MASC-enabled CNN-BiLSTM model performs better than traditional methods of accuracy 98.25%, precision 99.57%, and recall 97.53% for TP 80 with the MIT-BIH dataset. |
| format | Article |
| id | doaj-art-58187d25b9e34ea38bb1efa250d5b08b |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-58187d25b9e34ea38bb1efa250d5b08b2025-08-20T02:17:34ZengNature PortfolioScientific Reports2045-23222024-10-0114111710.1038/s41598-024-74993-2Enhancing heart disease classification with M2MASC and CNN-BiLSTM integration for improved accuracyVivek Pandey0Umesh Kumar Lilhore1Ranjan Walia2Roobaea Alroobaea3Majed Alsafyani4Abdullah M. Baqasah5Sultan Algarni6Department of Computer Science and Engineering, Chandigarh UniversityDepartment of Computer Science and Engineering, Chandigarh UniversityDepartment of Computer Science and Engineering, Chandigarh UniversityDepartment of Computer Science, College of Computers and Information Technology, Taif UniversityDepartment of Computer Science, College of Computers and Information Technology, Taif UniversityDepartment of Information Technology, College of Computers and Information Technology, Taif UniversityDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz UniversityAbstract Heart disease is a leading cause of death globally; therefore, accurate detection and classification are prominent, and several DL and ML methods have been developed over the last decade. However, the classical approaches may be prone to overfitting and under fitting issues, and the model performance may lag due to the unavailability of annotated datasets. To overcome these issues, the research proposed a model for heart disease detection and classification by integrating blockchain technology with a Modified mixed attention-enabled search optimizer-based CNN-Bidirectional Long Short-Term Memory (M2MASC enabled CNN-BiLSTM) model. The novel model incorporates a pre-trained VGG16 model to enhance feature extraction and improve the overall predictive accuracy. Leveraging the continuous monitoring capabilities of IoT devices, patient data is collected in real-time, providing a dynamic source to the CNN-BiLSTM model. Blockchain integration ensures stored health data’s security, transparency, and immutability, addresses privacy concerns, and promotes trust in the predictive system. The classifier parameters are tuned using the modified mixed attention and search optimization. The M2MASC-enabled CNN-BiLSTM model performs better than traditional methods of accuracy 98.25%, precision 99.57%, and recall 97.53% for TP 80 with the MIT-BIH dataset.https://doi.org/10.1038/s41598-024-74993-2Heart disease detectionBlockchainModified mixed attention mechanismModified mixed attention enabled search optimizer-based CNN-BiLSTMVGG 16 |
| spellingShingle | Vivek Pandey Umesh Kumar Lilhore Ranjan Walia Roobaea Alroobaea Majed Alsafyani Abdullah M. Baqasah Sultan Algarni Enhancing heart disease classification with M2MASC and CNN-BiLSTM integration for improved accuracy Scientific Reports Heart disease detection Blockchain Modified mixed attention mechanism Modified mixed attention enabled search optimizer-based CNN-BiLSTM VGG 16 |
| title | Enhancing heart disease classification with M2MASC and CNN-BiLSTM integration for improved accuracy |
| title_full | Enhancing heart disease classification with M2MASC and CNN-BiLSTM integration for improved accuracy |
| title_fullStr | Enhancing heart disease classification with M2MASC and CNN-BiLSTM integration for improved accuracy |
| title_full_unstemmed | Enhancing heart disease classification with M2MASC and CNN-BiLSTM integration for improved accuracy |
| title_short | Enhancing heart disease classification with M2MASC and CNN-BiLSTM integration for improved accuracy |
| title_sort | enhancing heart disease classification with m2masc and cnn bilstm integration for improved accuracy |
| topic | Heart disease detection Blockchain Modified mixed attention mechanism Modified mixed attention enabled search optimizer-based CNN-BiLSTM VGG 16 |
| url | https://doi.org/10.1038/s41598-024-74993-2 |
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