A Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer for cardiovascular disease detection and classification
Cardiovascular disease (CVD) is a common disorder frequently resulting in death. An increase in the death rate among adults is attributed to several factors, including smoking, high blood pressure, obesity, and cholesterol. Early diagnosis of CVDs can lower mortality rates. Algorithms that use machi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Healthcare Analytics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442523000400 |
| _version_ | 1797796054236659712 |
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| author | Siripuri Kiran Ganta Raghotham Reddy Girija S.P. Venkatramulu S. Kumar Dorthi Chandra Shekhar Rao V. |
| author_facet | Siripuri Kiran Ganta Raghotham Reddy Girija S.P. Venkatramulu S. Kumar Dorthi Chandra Shekhar Rao V. |
| author_sort | Siripuri Kiran |
| collection | DOAJ |
| description | Cardiovascular disease (CVD) is a common disorder frequently resulting in death. An increase in the death rate among adults is attributed to several factors, including smoking, high blood pressure, obesity, and cholesterol. Early diagnosis of CVDs can lower mortality rates. Algorithms that use machine learning and data mining offer the potential for finding risk variables and predicting CVD. Developing countries often need more CVD experts, and a high percentage of misdiagnosis. These concerns could be alleviated using an accurate and effective early-stage heart disease prediction system. This study explores the effectiveness of machine learning classifiers for diagnosing and detecting CVD. Several supervised machine-learning algorithms are investigated, and their performance and accuracy are compared. The Gradient Boosted Decision Tree (GBDT) with Binary Spotted Hyena Optimizer (BSHO) suggested in this work was used to rank and classify all attributes. Discrete optimization problems can be resolved using the binary form of SHO. The recommended method compresses the continuous location using a hyperbolic tangent function. The updated spotted hyena positions on the relevance score are utilized to find those with high heart disease predictions. The efficiency of the suggested model is then confirmed using the UCI dataset. The proposed GBDT-BSHO approach, with an accuracy of 97.89%, was significantly more effective than the comparative methods. |
| first_indexed | 2024-03-13T03:27:21Z |
| format | Article |
| id | doaj.art-6a9024ec59254a26a7e46e798aa5c359 |
| institution | Directory Open Access Journal |
| issn | 2772-4425 |
| language | English |
| last_indexed | 2024-03-13T03:27:21Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj.art-6a9024ec59254a26a7e46e798aa5c3592023-06-25T04:44:16ZengElsevierHealthcare Analytics2772-44252023-11-013100173A Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer for cardiovascular disease detection and classificationSiripuri Kiran0Ganta Raghotham Reddy1Girija S.P.2Venkatramulu S.3Kumar Dorthi4Chandra Shekhar Rao V.5Department of CSE(Networks), Kakatiya Institute of Technology and Science, Warangal, Telangana, India; Corresponding author.Department of ECE, Kakatiya Institute of Technology and Science, Warangal, Telangana, IndiaDepartment of ECE, Kakatiya Institute of Technology and Science, Warangal, Telangana, IndiaDepartment of CSE, Kakatiya Institute of Technology and Science, Warangal, Telangana, IndiaDepartment of CSE(Networks), Kakatiya Institute of Technology and Science, Warangal, Telangana, IndiaDepartment of CSE, Kakatiya Institute of Technology and Science, Warangal, Telangana, IndiaCardiovascular disease (CVD) is a common disorder frequently resulting in death. An increase in the death rate among adults is attributed to several factors, including smoking, high blood pressure, obesity, and cholesterol. Early diagnosis of CVDs can lower mortality rates. Algorithms that use machine learning and data mining offer the potential for finding risk variables and predicting CVD. Developing countries often need more CVD experts, and a high percentage of misdiagnosis. These concerns could be alleviated using an accurate and effective early-stage heart disease prediction system. This study explores the effectiveness of machine learning classifiers for diagnosing and detecting CVD. Several supervised machine-learning algorithms are investigated, and their performance and accuracy are compared. The Gradient Boosted Decision Tree (GBDT) with Binary Spotted Hyena Optimizer (BSHO) suggested in this work was used to rank and classify all attributes. Discrete optimization problems can be resolved using the binary form of SHO. The recommended method compresses the continuous location using a hyperbolic tangent function. The updated spotted hyena positions on the relevance score are utilized to find those with high heart disease predictions. The efficiency of the suggested model is then confirmed using the UCI dataset. The proposed GBDT-BSHO approach, with an accuracy of 97.89%, was significantly more effective than the comparative methods.http://www.sciencedirect.com/science/article/pii/S2772442523000400Gradient Boosted Decision TreeBinary Spotted Hyena OptimizerPredictive analyticsCardiovascular diseaseClassificationDiscrete optimization |
| spellingShingle | Siripuri Kiran Ganta Raghotham Reddy Girija S.P. Venkatramulu S. Kumar Dorthi Chandra Shekhar Rao V. A Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer for cardiovascular disease detection and classification Healthcare Analytics Gradient Boosted Decision Tree Binary Spotted Hyena Optimizer Predictive analytics Cardiovascular disease Classification Discrete optimization |
| title | A Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer for cardiovascular disease detection and classification |
| title_full | A Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer for cardiovascular disease detection and classification |
| title_fullStr | A Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer for cardiovascular disease detection and classification |
| title_full_unstemmed | A Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer for cardiovascular disease detection and classification |
| title_short | A Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer for cardiovascular disease detection and classification |
| title_sort | gradient boosted decision tree with binary spotted hyena optimizer for cardiovascular disease detection and classification |
| topic | Gradient Boosted Decision Tree Binary Spotted Hyena Optimizer Predictive analytics Cardiovascular disease Classification Discrete optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2772442523000400 |
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