Early prediction of heart disease with data analysis using supervised learning with stochastic gradient boosting
Abstract Heart diseases are consistently ranked among the top causes of mortality on a global scale. Early detection and accurate heart disease prediction can help effectively manage and prevent the disease. However, the traditional methods have failed to improve heart disease classification perform...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-10-01
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| Series: | Journal of Engineering and Applied Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s44147-023-00280-y |
| _version_ | 1797559416508121088 |
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| author | Anil Pandurang Jawalkar Pandla Swetcha Nuka Manasvi Pakki Sreekala Samudrala Aishwarya Potru Kanaka Durga Bhavani Pendem Anjani |
| author_facet | Anil Pandurang Jawalkar Pandla Swetcha Nuka Manasvi Pakki Sreekala Samudrala Aishwarya Potru Kanaka Durga Bhavani Pendem Anjani |
| author_sort | Anil Pandurang Jawalkar |
| collection | DOAJ |
| description | Abstract Heart diseases are consistently ranked among the top causes of mortality on a global scale. Early detection and accurate heart disease prediction can help effectively manage and prevent the disease. However, the traditional methods have failed to improve heart disease classification performance. So, this article proposes a machine learning approach for heart disease prediction (HDP) using a decision tree-based random forest (DTRF) classifier with loss optimization. Initially, preprocessing of the dataset with patient records with known labels is performed for the presence or absence of heart disease records. Then, train a DTRF classifier on the dataset using stochastic gradient boosting (SGB) loss optimization technique and evaluate the classifier’s performance using a separate test dataset. The results demonstrate that the proposed HDP-DTRF approach resulted in 86% of precision, 86% of recall, 85% of F1-score, and 96% of accuracy on publicly available real-world datasets, which are higher than traditional methods. |
| first_indexed | 2024-03-10T17:45:09Z |
| format | Article |
| id | doaj.art-a2d4f3e162944bdfa8f18a483e1f3ae4 |
| institution | Directory Open Access Journal |
| issn | 1110-1903 2536-9512 |
| language | English |
| last_indexed | 2024-03-10T17:45:09Z |
| publishDate | 2023-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Engineering and Applied Science |
| spelling | doaj.art-a2d4f3e162944bdfa8f18a483e1f3ae42023-11-20T09:33:29ZengSpringerOpenJournal of Engineering and Applied Science1110-19032536-95122023-10-0170111810.1186/s44147-023-00280-yEarly prediction of heart disease with data analysis using supervised learning with stochastic gradient boostingAnil Pandurang Jawalkar0Pandla Swetcha1Nuka Manasvi2Pakki Sreekala3Samudrala Aishwarya4Potru Kanaka Durga Bhavani5Pendem Anjani6Department of Information Technology, Malla Reddy Engineering College for Women (UGC-Autonomous)Department of Information Technology, Malla Reddy Engineering College for Women (UGC-Autonomous)Department of Information Technology, Malla Reddy Engineering College for Women (UGC-Autonomous)Department of Information Technology, Malla Reddy Engineering College for Women (UGC-Autonomous)Department of Information Technology, Malla Reddy Engineering College for Women (UGC-Autonomous)Department of Information Technology, Malla Reddy Engineering College for Women (UGC-Autonomous)Department of Information Technology, Malla Reddy Engineering College for Women (UGC-Autonomous)Abstract Heart diseases are consistently ranked among the top causes of mortality on a global scale. Early detection and accurate heart disease prediction can help effectively manage and prevent the disease. However, the traditional methods have failed to improve heart disease classification performance. So, this article proposes a machine learning approach for heart disease prediction (HDP) using a decision tree-based random forest (DTRF) classifier with loss optimization. Initially, preprocessing of the dataset with patient records with known labels is performed for the presence or absence of heart disease records. Then, train a DTRF classifier on the dataset using stochastic gradient boosting (SGB) loss optimization technique and evaluate the classifier’s performance using a separate test dataset. The results demonstrate that the proposed HDP-DTRF approach resulted in 86% of precision, 86% of recall, 85% of F1-score, and 96% of accuracy on publicly available real-world datasets, which are higher than traditional methods.https://doi.org/10.1186/s44147-023-00280-yHeart diseaseMachine learningDecision treeRandom forestStochastic gradient boostingLoss optimization |
| spellingShingle | Anil Pandurang Jawalkar Pandla Swetcha Nuka Manasvi Pakki Sreekala Samudrala Aishwarya Potru Kanaka Durga Bhavani Pendem Anjani Early prediction of heart disease with data analysis using supervised learning with stochastic gradient boosting Journal of Engineering and Applied Science Heart disease Machine learning Decision tree Random forest Stochastic gradient boosting Loss optimization |
| title | Early prediction of heart disease with data analysis using supervised learning with stochastic gradient boosting |
| title_full | Early prediction of heart disease with data analysis using supervised learning with stochastic gradient boosting |
| title_fullStr | Early prediction of heart disease with data analysis using supervised learning with stochastic gradient boosting |
| title_full_unstemmed | Early prediction of heart disease with data analysis using supervised learning with stochastic gradient boosting |
| title_short | Early prediction of heart disease with data analysis using supervised learning with stochastic gradient boosting |
| title_sort | early prediction of heart disease with data analysis using supervised learning with stochastic gradient boosting |
| topic | Heart disease Machine learning Decision tree Random forest Stochastic gradient boosting Loss optimization |
| url | https://doi.org/10.1186/s44147-023-00280-y |
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