Diagnosing the Stage of Hepatitis C Using Machine Learning
Butt, Muhammad Bilal and Alfayad, Majed and Saqib, Shazia and Khan, M. A. and Ahmad, Manir and Khan, Muhammad Adnan and Elmitwally, Nouh (2021) Diagnosing the Stage of Hepatitis C Using Machine Learning. Journal of Healthcare Engineering, 2021. p. 8062410. ISSN 2040-2295
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Abstract
Hepatitis C is a prevalent disease in the world. Around 3 to 4 million new cases of Hepatitis C are reported every year across the globe. Effective, timely prediction of the disease can help people know about their Stage of Hepatitis C. To identify the Stage of disease, various noninvasive serum biochemical markers and clinical information of the patients have been used. Machine learning techniques have been an effective alternative tool for determining the Stage of this chronic disease of the liver to prevent biopsy side effects. In this study, an Intelligent Hepatitis C Stage Diagnosis System (IHSDS) empowered with machine learning is presented to predict the Stage of Hepatitis C in a human using Artificial Neural Network (ANN). The dataset obtained from the UCI machine learning repository contains 29 features, out of which the 19 most reverent are selected to conduct the study; 70% of the dataset is used for training and 30% for validation purposes. The precision value is compared with the proposed IHSDS with previously presented models. The proposed IHSDS has achieved 98.89% precision during training and 94.44% precision during validation.
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.1155/2021/8062410 | ||||||
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| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence |
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| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology | ||||||
| Depositing User: | Nouh Elmitwally | ||||||
| Date Deposited: | 14 Dec 2021 13:57 | ||||||
| Last Modified: | 22 Mar 2023 12:00 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/12528 |
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