Juxtaposition on Classifiers in Modeling Hepatitis Diagnosis Data

Preetham Ganesh, Harsha Vardhini Vasu, Keerthanna Govindarajan Santhakumar, Raakheshsubhash Arumuga Rajan and K. R. Bindu ()
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Preetham Ganesh: Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Department of Computer Science and Engineering
Harsha Vardhini Vasu: Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Department of Computer Science and Engineering
Keerthanna Govindarajan Santhakumar: Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Department of Computer Science and Engineering
Raakheshsubhash Arumuga Rajan: Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Department of Computer Science and Engineering
K. R. Bindu: Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Department of Computer Science and Engineering

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 501-508 from Springer

Abstract: Abstract Machine Learning and Data Mining have been used extensively in the field of medical science. Approximately 2% of the world population, i.e., 3.9 million people are infected by Hepatitis C. This paper is an investigative study on the comparison of classification models—Support Vector Machine, Random Forest Classifier, Decision Tree Classifier, Logistic Regression, and Naive Bayes Classifier—modeling Hepatitis C Data based on various performance measures—Accuracy, Balanced Accuracy, Precision, Recall, F1-Measure, Matthews Correlation Coefficient and many more using R Programming Language. On normalizing the numerical attributes using Z-score Normalization and using the holdout method for the Train Test data split of 80–20%, the result shows that Random Forest outperforms the other classifiers with an accuracy of 90.7%, followed by Support Vector Machine, Logistic Regression, Decision Tree Classifier, and Naive Bayes Classifier.

Keywords: Hepatitis classification; UCI; Support vector machine; Decision tree classifier; Naive Bayes classifier; Random forest classifier; Logistic regression; K-Nearest neighbour classifier (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-41862-5_48

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