Early Thyroid Risk Prediction by Data Mining and Ensemble Classifiers
Thyroid disease is among the most prevalent endocrinopathies worldwide. As the thyroid gland controls human metabolism, thyroid illness is a matter of concern for human health. To save time and reduce error rates, an automatic, reliable, and accurate thyroid identification machine-learning (ML) syst...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Machine Learning and Knowledge Extraction |
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| Online Access: | https://www.mdpi.com/2504-4990/5/3/61 |
| _version_ | 1797579095555440640 |
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| author | Mohammad H. Alshayeji |
| author_facet | Mohammad H. Alshayeji |
| author_sort | Mohammad H. Alshayeji |
| collection | DOAJ |
| description | Thyroid disease is among the most prevalent endocrinopathies worldwide. As the thyroid gland controls human metabolism, thyroid illness is a matter of concern for human health. To save time and reduce error rates, an automatic, reliable, and accurate thyroid identification machine-learning (ML) system is essential. The proposed model aims to address existing work limitations such as the lack of detailed feature analysis, visualization, improvement in prediction accuracy, and reliability. Here, a public thyroid illness dataset containing 29 clinical features from the University of California, Irvine ML repository was used. The clinical features helped us to build an ML model that can predict thyroid illness by analyzing early symptoms and replacing the manual analysis of these attributes. Feature analysis and visualization facilitate an understanding of the role of features in thyroid prediction tasks. In addition, the overfitting problem was eliminated by 5-fold cross-validation and data balancing using the synthetic minority oversampling technique (SMOTE). Ensemble learning ensures prediction model reliability owing to the involvement of multiple classifiers in the prediction decisions. The proposed model achieved 99.5% accuracy, 99.39% sensitivity, and 99.59% specificity with the boosting method which is applicable to real-time computer-aided diagnosis (CAD) systems to ease diagnosis and promote early treatment. |
| first_indexed | 2024-03-10T22:32:05Z |
| format | Article |
| id | doaj.art-783299268792484badec0808fb72b85e |
| institution | Directory Open Access Journal |
| issn | 2504-4990 |
| language | English |
| last_indexed | 2024-03-10T22:32:05Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj.art-783299268792484badec0808fb72b85e2023-11-19T11:41:52ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902023-09-01531195121310.3390/make5030061Early Thyroid Risk Prediction by Data Mining and Ensemble ClassifiersMohammad H. Alshayeji0Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Safat, P.O. Box 5969, Kuwait City 13060, KuwaitThyroid disease is among the most prevalent endocrinopathies worldwide. As the thyroid gland controls human metabolism, thyroid illness is a matter of concern for human health. To save time and reduce error rates, an automatic, reliable, and accurate thyroid identification machine-learning (ML) system is essential. The proposed model aims to address existing work limitations such as the lack of detailed feature analysis, visualization, improvement in prediction accuracy, and reliability. Here, a public thyroid illness dataset containing 29 clinical features from the University of California, Irvine ML repository was used. The clinical features helped us to build an ML model that can predict thyroid illness by analyzing early symptoms and replacing the manual analysis of these attributes. Feature analysis and visualization facilitate an understanding of the role of features in thyroid prediction tasks. In addition, the overfitting problem was eliminated by 5-fold cross-validation and data balancing using the synthetic minority oversampling technique (SMOTE). Ensemble learning ensures prediction model reliability owing to the involvement of multiple classifiers in the prediction decisions. The proposed model achieved 99.5% accuracy, 99.39% sensitivity, and 99.59% specificity with the boosting method which is applicable to real-time computer-aided diagnosis (CAD) systems to ease diagnosis and promote early treatment.https://www.mdpi.com/2504-4990/5/3/61machine learningthyroiddata miningensemble modelfeature engineering |
| spellingShingle | Mohammad H. Alshayeji Early Thyroid Risk Prediction by Data Mining and Ensemble Classifiers Machine Learning and Knowledge Extraction machine learning thyroid data mining ensemble model feature engineering |
| title | Early Thyroid Risk Prediction by Data Mining and Ensemble Classifiers |
| title_full | Early Thyroid Risk Prediction by Data Mining and Ensemble Classifiers |
| title_fullStr | Early Thyroid Risk Prediction by Data Mining and Ensemble Classifiers |
| title_full_unstemmed | Early Thyroid Risk Prediction by Data Mining and Ensemble Classifiers |
| title_short | Early Thyroid Risk Prediction by Data Mining and Ensemble Classifiers |
| title_sort | early thyroid risk prediction by data mining and ensemble classifiers |
| topic | machine learning thyroid data mining ensemble model feature engineering |
| url | https://www.mdpi.com/2504-4990/5/3/61 |
| work_keys_str_mv | AT mohammadhalshayeji earlythyroidriskpredictionbydataminingandensembleclassifiers |