Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data
Abstract In recent years, the healthcare data system has expanded rapidly, allowing for the identification of important health trends and facilitating targeted preventative care. Heart disease remains a leading cause of death in developed countries, often leading to consequential outcomes such as de...
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| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85561-7 |
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| author | Lella Kranthi Kumar K. G. Suma Pamula Udayaraju Venkateswarlu Gundu Srihari Varma Mantena B. N. Jagadesh |
| author_facet | Lella Kranthi Kumar K. G. Suma Pamula Udayaraju Venkateswarlu Gundu Srihari Varma Mantena B. N. Jagadesh |
| author_sort | Lella Kranthi Kumar |
| collection | DOAJ |
| description | Abstract In recent years, the healthcare data system has expanded rapidly, allowing for the identification of important health trends and facilitating targeted preventative care. Heart disease remains a leading cause of death in developed countries, often leading to consequential outcomes such as dementia, which can be mitigated through early detection and treatment of cardiovascular issues. Continued research into preventing strokes and heart attacks is crucial. Utilizing the wealth of healthcare data related to cardiac ailments, a two-stage medical data classification and prediction model is proposed in this study. Initially, Binary Grey Wolf Optimization (BGWO) is used to cluster features, with the grouped information then utilized as input for the prediction model. An innovative 6-layered deep convolutional neural network (6LDCNNet) is designed for the classification of cardiac conditions. Hyper-parameter tuning for 6LDCNNet is achieved through an improved optimization method. The resulting model demonstrates promising performance on both the Cleveland dataset, achieving a convergence of 96% for assessing severity, and the echocardiography imaging dataset, with an impressive 98% convergence. This approach has the potential to aid physicians in diagnosing the severity of cardiac diseases, facilitating early interventions that can significantly reduce mortality associated with cardiovascular conditions. |
| format | Article |
| id | doaj-art-7e25f9833c9e4b279176a5d540149038 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7e25f9833c9e4b279176a5d5401490382025-01-12T12:24:24ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-025-85561-7Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient dataLella Kranthi Kumar0K. G. Suma1Pamula Udayaraju2Venkateswarlu Gundu3Srihari Varma Mantena4B. N. Jagadesh5School of Computer Science and Engineering, VIT-AP UniversitySchool of Computer Science and Engineering, VIT-AP UniversityDepartment of Computer Science and Engineering, School of Engineering and Sciences, SRM UniversityDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education FoundationDepartment of Computer Science and Engineering, SRKR Engineering CollegeSchool of Computer Science and Engineering, VIT-AP UniversityAbstract In recent years, the healthcare data system has expanded rapidly, allowing for the identification of important health trends and facilitating targeted preventative care. Heart disease remains a leading cause of death in developed countries, often leading to consequential outcomes such as dementia, which can be mitigated through early detection and treatment of cardiovascular issues. Continued research into preventing strokes and heart attacks is crucial. Utilizing the wealth of healthcare data related to cardiac ailments, a two-stage medical data classification and prediction model is proposed in this study. Initially, Binary Grey Wolf Optimization (BGWO) is used to cluster features, with the grouped information then utilized as input for the prediction model. An innovative 6-layered deep convolutional neural network (6LDCNNet) is designed for the classification of cardiac conditions. Hyper-parameter tuning for 6LDCNNet is achieved through an improved optimization method. The resulting model demonstrates promising performance on both the Cleveland dataset, achieving a convergence of 96% for assessing severity, and the echocardiography imaging dataset, with an impressive 98% convergence. This approach has the potential to aid physicians in diagnosing the severity of cardiac diseases, facilitating early interventions that can significantly reduce mortality associated with cardiovascular conditions.https://doi.org/10.1038/s41598-025-85561-7Heart diseaseImproved sailfish optimization algorithmConvolutional neural networkBinary Grey wolf optimizationEchocardiogram |
| spellingShingle | Lella Kranthi Kumar K. G. Suma Pamula Udayaraju Venkateswarlu Gundu Srihari Varma Mantena B. N. Jagadesh Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data Scientific Reports Heart disease Improved sailfish optimization algorithm Convolutional neural network Binary Grey wolf optimization Echocardiogram |
| title | Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data |
| title_full | Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data |
| title_fullStr | Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data |
| title_full_unstemmed | Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data |
| title_short | Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data |
| title_sort | clustering based binary grey wolf optimisation model with 6ldcnnet for prediction of heart disease using patient data |
| topic | Heart disease Improved sailfish optimization algorithm Convolutional neural network Binary Grey wolf optimization Echocardiogram |
| url | https://doi.org/10.1038/s41598-025-85561-7 |
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