N Komal Kumar, D Vigneswari
N Komal Kumar1*, D Vigneswari2
1Department of Computer Science and Engineering, St. Peter’s Institute of Higher Education and Research, Avadi, Chennai, India.
2Department of Information Technology, KCG College of Technology, Karapakkam, Chennai, India.
Volume - 12,
Issue - 8,
Year - 2019
Classification algorithms play a substantial role in analyzing and predicting infectious diseases. Hepatitis is a provocative condition of the liver tissues by developing a yellow tarnishing effect in the skin, the condition of hepatitis can be acute or chronic depending on the severity. This paper aims at comparing the performance of the classifiers in analyzing and predicting the infectious hepatitis disease. Logistic regression, random forest, decision tree, C4.5 and Multilayer perceptron classifiers are used in this analysis for prediction; the performance comparison metric is based on the TPR and accuracy values. In this performance comparison of predicting infectious hepatitis disease, Random forest classifier has achieved a higher accuracy of 90.3226% in correctly classifying the instances with an execution time of 0.14 sec than the other classifiers under analysis.
Cite this article:
N Komal Kumar, D Vigneswari. Hepatitis- Infectious Disease Prediction using Classification Algorithms. Research J. Pharm. and Tech 2019; 12(8): 3720-3725. doi: 10.5958/0974-360X.2019.00636.X
N Komal Kumar, D Vigneswari. Hepatitis- Infectious Disease Prediction using Classification Algorithms. Research J. Pharm. and Tech 2019; 12(8): 3720-3725. doi: 10.5958/0974-360X.2019.00636.X Available on: https://rjptonline.org/AbstractView.aspx?PID=2019-12-8-26