An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning Model
The increasing prevalence of cardiovascular disorders has created an imperative need for accurate diagnoses. Despite the emergence of numerous techniques for disease classification and secure data transmission, a prevailing shortcoming is the lack of precision in decision-making. This study aimed to...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
D. G. Pylarinos
2023-12-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/6503 |
| _version_ | 1797403575854301184 |
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| author | Priyanka Dhaka Ruchi Sehrawat Priyanka Bhutani |
| author_facet | Priyanka Dhaka Ruchi Sehrawat Priyanka Bhutani |
| author_sort | Priyanka Dhaka |
| collection | DOAJ |
| description | The increasing prevalence of cardiovascular disorders has created an imperative need for accurate diagnoses. Despite the emergence of numerous techniques for disease classification and secure data transmission, a prevailing shortcoming is the lack of precision in decision-making. This study aimed to address this critical issue by introducing an innovative disease prediction model that uses a hybrid classifier. The proposed hybrid classifier combined deep Bidirectional Long-Short-Term Memory (deep Bi LSTM) and deep Convolutional Neural Network (deep CNN).To further improve its performance, the proposed approach employed hybridized swarm optimization to fine-tune fusion parameters and optimize the learning model for enhanced accuracy. This study focused on heart disease as its central concern, strengthening data security through the implementation of Diffi-Huffman based on Elliptic Curve Cryptography (ECC) during data transmission. The resulting automatic disease prediction model adopted the hybrid deep classifier, which was born from the amalgamation of two components: the interactive hunt-deep CNN classifier and the WoM-deep Bi LSTM. The proposed hybrid learning model achieved impressive accuracy, F-measure, sensitivity, and specificity of 97.716%, 97.848%, 98.021%, and 97.807%, respectively, marking a significant advance in the realm of cardiovascular disease prediction.
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| first_indexed | 2024-03-09T02:40:27Z |
| format | Article |
| id | doaj.art-7c969d838bf849d6af7ab6c7e1da524c |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-03-09T02:40:27Z |
| publishDate | 2023-12-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-7c969d838bf849d6af7ab6c7e1da524c2023-12-06T05:56:32ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362023-12-0113610.48084/etasr.6503An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning ModelPriyanka Dhaka 0Ruchi Sehrawat1Priyanka Bhutani2University School of Information and Communication Technology, GGSIPU, India | Maharaja Surajmal Institute, IndiaUniversity School of Information and Communication Technology, GGSIPU, IndiaUniversity School of Information and Communication Technology, GGSIPU, IndiaThe increasing prevalence of cardiovascular disorders has created an imperative need for accurate diagnoses. Despite the emergence of numerous techniques for disease classification and secure data transmission, a prevailing shortcoming is the lack of precision in decision-making. This study aimed to address this critical issue by introducing an innovative disease prediction model that uses a hybrid classifier. The proposed hybrid classifier combined deep Bidirectional Long-Short-Term Memory (deep Bi LSTM) and deep Convolutional Neural Network (deep CNN).To further improve its performance, the proposed approach employed hybridized swarm optimization to fine-tune fusion parameters and optimize the learning model for enhanced accuracy. This study focused on heart disease as its central concern, strengthening data security through the implementation of Diffi-Huffman based on Elliptic Curve Cryptography (ECC) during data transmission. The resulting automatic disease prediction model adopted the hybrid deep classifier, which was born from the amalgamation of two components: the interactive hunt-deep CNN classifier and the WoM-deep Bi LSTM. The proposed hybrid learning model achieved impressive accuracy, F-measure, sensitivity, and specificity of 97.716%, 97.848%, 98.021%, and 97.807%, respectively, marking a significant advance in the realm of cardiovascular disease prediction. https://etasr.com/index.php/ETASR/article/view/6503cardiovascular disease predictionelliptic curve cryptographyinteractive hunt-deep CNNWoM-deep bi LSTMDiffi-Huffman |
| spellingShingle | Priyanka Dhaka Ruchi Sehrawat Priyanka Bhutani An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning Model Engineering, Technology & Applied Science Research cardiovascular disease prediction elliptic curve cryptography interactive hunt-deep CNN WoM-deep bi LSTM Diffi-Huffman |
| title | An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning Model |
| title_full | An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning Model |
| title_fullStr | An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning Model |
| title_full_unstemmed | An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning Model |
| title_short | An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning Model |
| title_sort | innovative approach to cardiovascular disease prediction a hybrid deep learning model |
| topic | cardiovascular disease prediction elliptic curve cryptography interactive hunt-deep CNN WoM-deep bi LSTM Diffi-Huffman |
| url | https://etasr.com/index.php/ETASR/article/view/6503 |
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