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- W4285178574 abstract "AbstractHepatitis 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.KeywordsClassificationFeature selectionHepatitis CMachine learningPrediction" @default.
- W4285178574 created "2022-07-14" @default.
- W4285178574 creator A5011587884 @default.
- W4285178574 date "2022-01-01" @default.
- W4285178574 modified "2023-10-16" @default.
- W4285178574 title "A Comparison of Machine Learning Techniques for Categorization of Blood Donors Having Chronic Hepatitis C Infection" @default.
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- W4285178574 doi "https://doi.org/10.1007/978-981-16-7610-9_54" @default.
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