Machine learning models for diagnostic classification of hepatitis C tests
Publish place: Frontiers in Health Informatics، Vol: 10، Issue: 1
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJIMI-10-1_020
تاریخ نمایه سازی: 30 مرداد 1401
Abstract:
Introduction: Hepatitis C is a chronic infection caused by hepatitis c virus - a blood borne virus. Therefore, the infection occurs through exposure to small quantities of blood. It has been estimated by World Health Organization (WHO) to have affected ۷۱ million people worldwide. This infection costs individual, groups and government a lot because no vaccine has been gotten yet for the treatment. This disease is likely to continue to affect more people because it’s long asymptotic phase which makes its early detection not feasible.Material and Methods: In this study, we have presented machine learning models to automatically classify the diagnosis test of hepatitis and also ranked the test features in order to know how they contribute to the classification which help in decision making process by the health care industry. The synthetic minority oversampling technique (SMOTE) was used to solve the problem of imbalance dataset.Results: The models were evaluated based on metrics such as Matthews correlation coefficient, F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve. We found that using SMOTE techniques helped raise performance of the predictive models. Also, random forest (RF) had the best performance based on Matthews correlation coefficient (۰.۹۹), F-measure (۰.۹۹), Precision-Recall curve (۱.۰۰) and Receiver Operating Characteristic Area Under Curve (۰.۹۹).Conclusion: This discovery has the potential to impact on clinical practice, when health workers aim at classifying diagnosis result of disease at its early stage.
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Authors
Oladosu Oyebisi Oladimeji
Department of Computer Science, University of Ibadan, Ibadan, Nigeria
Abimbola Oladimeji
Department of Chemistry, University of Ibadan, Ibadan, Nigeria
Oladimeji Olayanju
Department of Computer Science and Information Technology, University of Bowen, Iwo, Nigeria