Table 1.
Summarized results of contemporary state-of-the-art techniques.
| Year | Data description | Approaches | Accuracy (%) |
|---|---|---|---|
| Reference [19], 2015 | Parkinson's disease dataset | SVM | 99.49 |
| ANN | 96.77 | ||
| KNN | 96.07 | ||
| Decision Tree | 95 | ||
| Random Forest | 90.26 | ||
| Naive Bayes | 74.31 | ||
| Reference [8], 2015 | Dengue dataset | Naive Bayes | 100 |
| Reference [9], 2015 | Cleveland database of UCI repository | Decision Tree (J48) | 56.76 |
| Reference [20], 2014 | Parkinson's disease dataset | SVM | 85.48 |
| Reference [22], 2013 | Lung cancer dataset | Decision Tree | 90.59 |
| Heart dataset | Naive Bayes | 80.75 | |
| Dermatology | Naive Bayes | 82.32 | |
| Reference [17], 2013 | Cancer | Decision Tree | 97.77 |
| Blood bank sector | J48 | 89.9 | |
| Diabetes mellitus | C4.5 | 82.6 | |
| HIV/AIDS | C4.5 | 81.8 | |
| Tuberculosis | KNN | 78 | |
| Hepatitis C | Decision Rule | 73.20 | |
| Heart disease | Naive Bayes | 60 | |
| Reference [10], 2013 | Heart disease | Naive Bayes | 74 |
| Reference [18], 2013 | Heart disease dataset (Cleveland Clinic Foundation) | CART | 83.49 |
| Reference [7], 2013 | Cleveland Heart Disease database | Naive Bayes | 95 |
| Reference [6], 2012 | Lung cancer (SEER dataset) | Decision Tree (C4.5) | 94.43 |
| Reference [21], 2010 | Heart disease dataset | Naive Bayes | 52.33 |