Academic Journal
A novel intelligent machine learning system for coronary heart disease diagnosis.
| Τίτλος: | A novel intelligent machine learning system for coronary heart disease diagnosis. |
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| Συγγραφείς: | Alsafi, Haedar Emad Sharef, Ocan, Osman Nuri |
| Πηγή: | Applied Nanoscience; Mar2023, Vol. 13 Issue 3, p2473-2480, 8p |
| Θεματικοί όροι: | Coronary disease, Heart disease diagnosis, Machine learning, Myocardial infarction, Random forest algorithms |
| Περίληψη: | Coronary heart disease (CHD) is a significant medical disorder and one of the most prevalent forms of heart disease. Owing to the reality that a heart attack will happen without notice, an insightful screening system is inevitable. This paper investigates a new CHD detection approach built on an optimization machine learning technique, such as classifier ensembles. To boost the efficiency of our system, we used the Feature-Selector optimization model to select the best subset of CHD features. Second, to solve the problem of imbalanced CHD data, we used optimized SMOTE over-sampling, a highly efficient approach embedded with an optimization model. The class label estimation of three optimization learners, namely random forest, XGBoost API optimization, and SVM optimization model, is integrated in a stacked architecture. The identification model is validated using data from CHD patients. Finally, in terms of precision, F1, and ROC-Curve, our detection model outperformed existing ones focused on optimization models ensembles and individual classifiers. With random forest optimization, we achieved 90% accuracy, and with the XGBoost API optimization model, we achieved 89% accuracy. In contrast to previous reported research in the existing literature, this analysis indicates that our proposed model makes a substantial contribution. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Nanoscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s13204-021-01992-4 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Header | DbId: edb DbLabel: Complementary Index An: 162233442 RelevancyScore: 932 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 931.94970703125 |
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| Items | – Name: Title Label: Title Group: Ti Data: A novel intelligent machine learning system for coronary heart disease diagnosis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Alsafi%2C+Haedar+Emad+Sharef%22">Alsafi, Haedar Emad Sharef</searchLink><br /><searchLink fieldCode="AR" term="%22Ocan%2C+Osman+Nuri%22">Ocan, Osman Nuri</searchLink> – Name: TitleSource Label: Source Group: Src Data: Applied Nanoscience; Mar2023, Vol. 13 Issue 3, p2473-2480, 8p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Coronary+disease%22">Coronary disease</searchLink><br /><searchLink fieldCode="DE" term="%22Heart+disease+diagnosis%22">Heart disease diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Myocardial+infarction%22">Myocardial infarction</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Coronary heart disease (CHD) is a significant medical disorder and one of the most prevalent forms of heart disease. Owing to the reality that a heart attack will happen without notice, an insightful screening system is inevitable. This paper investigates a new CHD detection approach built on an optimization machine learning technique, such as classifier ensembles. To boost the efficiency of our system, we used the Feature-Selector optimization model to select the best subset of CHD features. Second, to solve the problem of imbalanced CHD data, we used optimized SMOTE over-sampling, a highly efficient approach embedded with an optimization model. The class label estimation of three optimization learners, namely random forest, XGBoost API optimization, and SVM optimization model, is integrated in a stacked architecture. The identification model is validated using data from CHD patients. Finally, in terms of precision, F1, and ROC-Curve, our detection model outperformed existing ones focused on optimization models ensembles and individual classifiers. With random forest optimization, we achieved 90% accuracy, and with the XGBoost API optimization model, we achieved 89% accuracy. In contrast to previous reported research in the existing literature, this analysis indicates that our proposed model makes a substantial contribution. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Applied Nanoscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s13204-021-01992-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 2473 Subjects: – SubjectFull: Coronary disease Type: general – SubjectFull: Heart disease diagnosis Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Myocardial infarction Type: general – SubjectFull: Random forest algorithms Type: general Titles: – TitleFull: A novel intelligent machine learning system for coronary heart disease diagnosis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Alsafi, Haedar Emad Sharef – PersonEntity: Name: NameFull: Ocan, Osman Nuri IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 21905509 Numbering: – Type: volume Value: 13 – Type: issue Value: 3 Titles: – TitleFull: Applied Nanoscience Type: main |
| ResultId | 1 |