| Machine Learning Techniques | Author | Year | Disease | Resource of Data Set | Tool | Accuracy |
| Naive Bayes | Iyer et al. | 2015 | Diabetes Disease | Pima Indian Diabetes dataset | WEKA | 79.5652% |
| J48 | 76.9565% | |||||
| CART | Sen and Dash | 2014 | Diabetes Disease | Pima Indian Diabetes dataset from UCI | WEKA | 78.646% |
| Adaboost | 77.864% | |||||
| Logiboost | 77.479% | |||||
| Grading | 66.406% | |||||
| SVM | Kumari and Chitra | 2013 | Diabetes Disease | UCI | MATLAB 2010a | 78% |
| Naive Bayes | Sarwar and Sharma | 2012 | Diabetes type-2 | Different Sectors of Society in India | MATLAB with SQL Server | 95% |
| GA + Fuzzy Logic | Ephzibah | 2011 | Diabetes disease | UCI | MATLAB | 87% |