Table 2.
Total number of algorithms applied for predictive analysis
| AI/ML algorithms and approaches | Total count |
|---|---|
| SVM | 13 |
| RF | 12 |
| XGBoost | 5 |
| LR | 5 |
| NB | 5 |
| DT | 5 |
| ANN | 4 |
| k-nearest neighbor (K-NN) | 4 |
| AB | 3 |
| Gradient Boosting | 3 |
| LDA | 2 |
| QDA | 2 |
| GPC | 2 |
| Clustering (Unsupervised) | 2 |
| Elastic net regularized generalized linear model | 1 |
| BART | 1 |
| Bayesian Networks | 1 |
| Greedy Thick Thinning algorithm | 1 |
| NMF | 1 |
| C4.5 | 1 |
| FCA | 1 |
| MLR | 1 |
| GA | 1 |
| Logit Boost | 1 |
| AVA,Dx | 1 |
| OncoCast-MPM machine-learning risk-prediction model | 1 |
| CADD | 1 |
| Very Efficient Substitution Transposition (VEST) | 1 |
| Random committee ensemble learning | 1 |
| DNNs | 1 |
| MERGE | 1 |
| EM | 1 |