| Machine Learning Techniques | Author | Year | Disease | Resources of Data Set | Tool | Accuracy |
| Bayes Net | Otoom et al. | 2015 | CAD (Coronary artery disease) | UCI | WEKA | 84.5% |
| SVM | 85.1% | |||||
| FT | 84.5% | |||||
| Naive Bayes | Vembandasamy et al. | 2015 | Heart Disease | Diabetic Research Institute in Chennai | WEKA | 86.419% |
| Naive Bayes | Chaurasia and Pal | 2013 | Heart Disease | UCI | WEKA | 82.31% |
| J48 | 84.35% | |||||
| Bagging | 85.03% | |||||
| SVM | Parthiban and Srivatsa | 2012 | Heart disease | Research institute in Chennai | WEKA | 94.60% |
| Naive Bayes | 74% | |||||
| Hybrid Technique (GA + SVM) | Tan et al. | 2009 | Heart disease | UCI | LIBSVM and WEKA | 84.07% |