| Machine Learning Techniques | Author | Year | Disease | Resource of Data Set | Tool | Accuracy |
| SVM | Vijayarani and Dhayanand | 2015 | Liver Disease | ILPD from UCI | MATLAB | 79.66% |
| Naive Bayes | 61.28% | |||||
| J48 | Gulia et al. | 2014 | Liver Disease | UCI | WEKA | 70.669% |
| MLP | 70.8405% | |||||
| Random Forest | 71.8696% | |||||
| SVM | 71.3551% | |||||
| Bayesian Network | 69.1252% | |||||
| Naive Bayes | Rajeswari and Reena | 2010 | Liver Disease | UCI | WEKA | 96.52% |
| K Star | 83.47% | |||||
| FT tree | 97.10% |