TY  - JOUR
TI  - Liver Cancer Detection
AU  - L. S. Rohith Anand
AU  -  B. Shannmuka
AU  -  R. Uday Chowdary
AU  -  K. Satya Sai Krishna
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2019/02/28
PY  - 2019
DO  - https://doi.org/10.32628/CSEIT183818
UR  - https://ijsrcseit.com/CSEIT183818
VL  - 5
IS  - 1
SP  - 221	
EP  - 228
AB  - 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.