Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique
Background: Heart disease is one of the most frequent chronic ailments people suffer. Early identification can lower death rates by avoiding or lowering cardiovascular disease (CVD) severity. For detecting risk indicators, machine learning algorithms are a potential way. Methods: To acquire accurate...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Intelligent Systems with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266730532200059X |
| _version_ | 1811261556272398336 |
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| author | Prasannavenkatesan Theerthagiri |
| author_facet | Prasannavenkatesan Theerthagiri |
| author_sort | Prasannavenkatesan Theerthagiri |
| collection | DOAJ |
| description | Background: Heart disease is one of the most frequent chronic ailments people suffer. Early identification can lower death rates by avoiding or lowering cardiovascular disease (CVD) severity. For detecting risk indicators, machine learning algorithms are a potential way. Methods: To acquire accurate cardiac disease prediction, this work introduces a recursive feature elimination-based gradient boosting (RFE-GB) approach. The outcomes were evaluated using the patients' health records, including crucial CVD characteristics. The prediction model was built using many additional machine learning approaches, and the results were compared to the suggested model. Results: The combined recursive feature removal and gradient boosting approach deliver the maximum accuracy, according to the findings of this suggested model (88.8 %). Furthermore, the presented RFE-GB method was determined to be superior and had acquired a significant gain over previous strategies, with an area under the curve of 0.84. Conclusion: The developed RFE-GB method will thus be a useful model for predicting and treating CVD. |
| first_indexed | 2024-04-12T19:06:38Z |
| format | Article |
| id | doaj.art-5ed1ae7fad394e9d93d6f9e12b6a4602 |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-04-12T19:06:38Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-5ed1ae7fad394e9d93d6f9e12b6a46022022-12-22T03:20:00ZengElsevierIntelligent Systems with Applications2667-30532022-11-0116200121Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination techniquePrasannavenkatesan Theerthagiri0Department of Computer Science and Engineering, GITAM School of Technology, GITAM University, Bengaluru, IndiaBackground: Heart disease is one of the most frequent chronic ailments people suffer. Early identification can lower death rates by avoiding or lowering cardiovascular disease (CVD) severity. For detecting risk indicators, machine learning algorithms are a potential way. Methods: To acquire accurate cardiac disease prediction, this work introduces a recursive feature elimination-based gradient boosting (RFE-GB) approach. The outcomes were evaluated using the patients' health records, including crucial CVD characteristics. The prediction model was built using many additional machine learning approaches, and the results were compared to the suggested model. Results: The combined recursive feature removal and gradient boosting approach deliver the maximum accuracy, according to the findings of this suggested model (88.8 %). Furthermore, the presented RFE-GB method was determined to be superior and had acquired a significant gain over previous strategies, with an area under the curve of 0.84. Conclusion: The developed RFE-GB method will thus be a useful model for predicting and treating CVD.http://www.sciencedirect.com/science/article/pii/S266730532200059XHeart diseasesFeature rankingMachine learningRecursive feature eliminationGradient boosting |
| spellingShingle | Prasannavenkatesan Theerthagiri Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique Intelligent Systems with Applications Heart diseases Feature ranking Machine learning Recursive feature elimination Gradient boosting |
| title | Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique |
| title_full | Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique |
| title_fullStr | Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique |
| title_full_unstemmed | Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique |
| title_short | Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique |
| title_sort | predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique |
| topic | Heart diseases Feature ranking Machine learning Recursive feature elimination Gradient boosting |
| url | http://www.sciencedirect.com/science/article/pii/S266730532200059X |
| work_keys_str_mv | AT prasannavenkatesantheerthagiri predictiveanalysisofcardiovasculardiseaseusinggradientboostingbasedlearningandrecursivefeatureeliminationtechnique |