Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection
Around 1.5 million new cases of Hepatitis C Virus (HCV) are diagnosed globally each year (World Health Organization, 2023). Consequently, there is a pressing need for early diagnostic methods for HCV. This study investigates the prognostic accuracy of several ensemble machine learning (ML) models fo...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/45086/ http://umpir.ump.edu.my/id/eprint/45086/1/Comparative%20investigation%20of%20bagging%20enhanced%20machine%20learning.pdf |
| _version_ | 1848827252375027712 |
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| author | Tusher, Ekramul Haque Mohd Arfian, Ismail Akib, Abdullah Gabralla, Lubna A. Ashraf Osman, Ibrahim Hafizan, Mat Som Muhammad Akmal, Remli |
| author_facet | Tusher, Ekramul Haque Mohd Arfian, Ismail Akib, Abdullah Gabralla, Lubna A. Ashraf Osman, Ibrahim Hafizan, Mat Som Muhammad Akmal, Remli |
| author_sort | Tusher, Ekramul Haque |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Around 1.5 million new cases of Hepatitis C Virus (HCV) are diagnosed globally each year (World Health Organization, 2023). Consequently, there is a pressing need for early diagnostic methods for HCV. This study investigates the prognostic accuracy of several ensemble machine learning (ML) models for diagnosing HCV infection. The study utilizes a dataset comprising demographic information of 615 individuals suspected of having HCV infection. Additionally, the research employs oversampling and undersampling techniques to address class imbalances in the dataset and conducts feature reduction using the F-test in one-way analysis of variance. Ensemble ML methods, including Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB), and Decision Tree (DT), are used to predict HCV infection. The performance of these ensemble methods is evaluated using metrics such as accuracy, recall, precision, F1 score, G-mean, balanced accuracy, cross-validation (CV), area under the curve (AUC), standard deviation, and error rate. Compared with previous studies, the Bagging k-NN model demonstrated superior performance under oversampling conditions, achieving 98.37% accuracy, 98.23% CV score, 97.67% precision, 97.93% recall, 98.18% selectivity, 97.79% F1 score, 98.06% balanced accuracy, 98.05% G-mean, a 1.63% error rate, 0.98 AUC, and a standard deviation of 0.192. This study highlights the potential of ensemble ML approaches in improving the diagnosis of HCV. The findings provide a foundation for developing accurate predictive methods for HCV diagnosis. |
| first_indexed | 2025-11-15T03:57:45Z |
| format | Article |
| id | ump-45086 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:57:45Z |
| publishDate | 2025 |
| publisher | Public Library of Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-450862025-07-15T03:20:35Z http://umpir.ump.edu.my/id/eprint/45086/ Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection Tusher, Ekramul Haque Mohd Arfian, Ismail Akib, Abdullah Gabralla, Lubna A. Ashraf Osman, Ibrahim Hafizan, Mat Som Muhammad Akmal, Remli QA75 Electronic computers. Computer science QA76 Computer software RA Public aspects of medicine Around 1.5 million new cases of Hepatitis C Virus (HCV) are diagnosed globally each year (World Health Organization, 2023). Consequently, there is a pressing need for early diagnostic methods for HCV. This study investigates the prognostic accuracy of several ensemble machine learning (ML) models for diagnosing HCV infection. The study utilizes a dataset comprising demographic information of 615 individuals suspected of having HCV infection. Additionally, the research employs oversampling and undersampling techniques to address class imbalances in the dataset and conducts feature reduction using the F-test in one-way analysis of variance. Ensemble ML methods, including Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB), and Decision Tree (DT), are used to predict HCV infection. The performance of these ensemble methods is evaluated using metrics such as accuracy, recall, precision, F1 score, G-mean, balanced accuracy, cross-validation (CV), area under the curve (AUC), standard deviation, and error rate. Compared with previous studies, the Bagging k-NN model demonstrated superior performance under oversampling conditions, achieving 98.37% accuracy, 98.23% CV score, 97.67% precision, 97.93% recall, 98.18% selectivity, 97.79% F1 score, 98.06% balanced accuracy, 98.05% G-mean, a 1.63% error rate, 0.98 AUC, and a standard deviation of 0.192. This study highlights the potential of ensemble ML approaches in improving the diagnosis of HCV. The findings provide a foundation for developing accurate predictive methods for HCV diagnosis. Public Library of Science 2025-06-26 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/45086/1/Comparative%20investigation%20of%20bagging%20enhanced%20machine%20learning.pdf Tusher, Ekramul Haque and Mohd Arfian, Ismail and Akib, Abdullah and Gabralla, Lubna A. and Ashraf Osman, Ibrahim and Hafizan, Mat Som and Muhammad Akmal, Remli (2025) Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection. PLoS ONE, 20 (e0326488). pp. 1-44. ISSN 1932-6203. (Published) https://doi.org/10.1371/journal.pone.0326488 https://doi.org/10.1371/journal.pone.0326488 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software RA Public aspects of medicine Tusher, Ekramul Haque Mohd Arfian, Ismail Akib, Abdullah Gabralla, Lubna A. Ashraf Osman, Ibrahim Hafizan, Mat Som Muhammad Akmal, Remli Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection |
| title | Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection |
| title_full | Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection |
| title_fullStr | Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection |
| title_full_unstemmed | Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection |
| title_short | Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection |
| title_sort | comparative investigation of bagging enhanced machine learning for early detection of hcv infections using class imbalance technique with feature selection |
| topic | QA75 Electronic computers. Computer science QA76 Computer software RA Public aspects of medicine |
| url | http://umpir.ump.edu.my/id/eprint/45086/ http://umpir.ump.edu.my/id/eprint/45086/ http://umpir.ump.edu.my/id/eprint/45086/ http://umpir.ump.edu.my/id/eprint/45086/1/Comparative%20investigation%20of%20bagging%20enhanced%20machine%20learning.pdf |