Machine Learning Techniques | Author | Year | Disease | Resource of Data Set | Tool | Accuracy |
Naive Bayes | Ba-Alwi and Hintaya, | 2013 | Hepatitis Disease | UCI | WEKA | 96.52% |
Naive Bayes updateable | 84% | |||||
FT | 87.10% | |||||
K Star | 83.47% | |||||
J48 | 83% | |||||
LMT | 83.6% | |||||
NN | 70.41% | |||||
Naive Bayes | Karlik | 2011 | Hepatitis Disease | UCI | Rapid Miner | 97% |
Feed forward NN with Back propagation | 98% | |||||
C4.5 | Sathyadevi | 2011 | Hepatitis Disease | UCI | WEKA | 71.4% |
ID3 | 64.8% | |||||
CART | 83.2% |