Liver Cancer Detection

Authors(4) :-L. S. Rohith Anand, B. Shannmuka, R. Uday Chowdary, K. Satya Sai Krishna

Machine learning techniques play an important role in building predictive models by learning from Electronic Health Records (EHR). Predictive models building from Electronic Health Records still remains as a challenge as the clinical healthcare data is complex in nature and analysing such data is a difficult task. This paper proposes prediction models built using random forest ensemble by using three different classifiers viz. J48, C4.5 and Naive Bayes classifiers. The proposed random forest ensemble was used for classifying four stages of liver cancer. Using a feature selection method the reliable features are identified and this subset serves as input for the ensemble of classifiers. Further a majority voting mechanism is used to predict the class labels of the liver cancer data. Experiments were conducted by varying the number of decision trees generated using the J48, C4.5 and Naive Bayes classifiers and compared with the classification made using decision stump and Adaboost algorithms.

Authors and Affiliations

L. S. Rohith Anand
CSE, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
B. Shannmuka
CSE, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
R. Uday Chowdary
CSE, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
K. Satya Sai Krishna
CSE, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India

Ensemble, Feature Selection, C4.5, J48 and Random Forest

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Publication Details

Published in : Volume 5 | Issue 1 | January-February 2019
Date of Publication : 2019-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 221-228
Manuscript Number : CSEIT183818
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

L. S. Rohith Anand, B. Shannmuka, R. Uday Chowdary, K. Satya Sai Krishna, "Liver Cancer Detection", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.221-228, January-February-2019. Available at doi :
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