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.
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References
M. Ramasamy, S. Selvaraj, and M. Mayilvaganan. An empirical analysis of decision tree algorithms: Modeling hepatitis data. In 2015 IEEE International Conference on Engineering and Technology (ICETECH), pages 1–4, 2015.
Huda Yasin, Tahseen A. Jilani, and Madiha Danish. Hepatitis-c classification using data mining techniques. International Journal of Computer Applications, 24(3):1–6, 2011.
World Health Organization et al. Global hepatitis report 2017. World Health Organization, 2017.
A.H. Roslina and A. Noraziah. Prediction of hepatitis prognosis using support vector machines and wrapper method. In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, volume 5, pages 2209–2211, 2010.
S. Ekız and P. Erdogmus. Comparative study of heart disease classification. In 2017 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), pages 1–4, 2017.
Dheeru Dua and Casey Graff. UCI machine learning repository, 2017.
Strother H. Walker and David B. Duncan. Estimation of the probability of an event as a function of several independent variables. Biometrika, 54(1/2):167–179, 1967.
David J. Hand and Keming Yu. Idiot’s bayes: Not so stupid after all? International Statistical Review/Revue Internationale de Statistique, 69(3):385–398, 2001.
Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine Learning, 20(3):273–297, 1995.
J. Ross Quinlan. Induction of decision trees. Machine Learning, 1(1):81–106, 1986.
Leo Breiman. Random forests. Machine Learning, 45(1):5–32, 2001.
Jiawei Han, Jian Pei, and Micheline Kamber. Data mining: concepts and techniques. Elsevier, 2011.
Tom Fawcett. An introduction to roc analysis. Pattern Recognition Letters, 27(8):861–874, 2006.
B.W. Matthews. Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochimica et Biophysica Acta (BBA) - Protein Structure, 405(2):442 – 451, 1975.
David Martin Powers. Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. 2011.
Donald B. Rubin. Statistical matching using file concatenation with adjusted weights and multiple imputations. Journal of Business Economic Statistics, 4(1):87–94, 1986.
D. Freedman, R. Pisani, and R. Purves. Statistics: Fourth International Student Edition. International student edition. W.W. Norton & Company, 2007.
Vikas K Vijayan, KR Bindu, and Latha Parameswaran. A comprehensive study of text classification algorithms. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pages 1109–1113. IEEE, 2017.
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Ganesh, P., Vasu, H.V., Santhakumar, K.G., Rajan, R.A., Bindu, K.R. (2020). Juxtaposition on Classifiers in Modeling Hepatitis Diagnosis Data. In: Smys, S., Iliyasu, A.M., Bestak, R., Shi, F. (eds) New Trends in Computational Vision and Bio-inspired Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-41862-5_48
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DOI: https://doi.org/10.1007/978-3-030-41862-5_48
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