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A Comparison of Machine Learning Techniques for Categorization of Blood Donors Having Chronic Hepatitis C Infection
Published in Springer Science and Business Media Deutschland GmbH
Volume: 101
Pages: 731 - 742
Hepatitis C is a liver disease whose infection is often silent and can lead to fibrosis or cirrhosis if it becomes chronic and goes undetected. It is generally spread through blood-to-blood contact. Hence, it is important to accurately classify blood donors as healthy blood donor or a person having Hepatitis C infection before blood transfusion happens. Nowadays, machine learning has been used in various domains including health care for accurate and fast results. This paper proposes a framework for accurate classification of blood donors using five machine learning algorithms, namely logistic regression, support vector machine, k-nearest neighbours, decision tree, and neural networks. The backward elimination technique is implemented for feature selection to improve the classification accuracy. The experimental results show that k-nearest neighbours perform better with the testing accuracy of 94.3% than other classifiers. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About the journal
JournalLecture Notes on Data Engineering and Communications Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Open AccessNo