Table 1.
Model type of the 152 models developed in the 62 included publications
| Model characteristics | All models (n = 152) |
|---|---|
| n (%) | |
| Regression-based models | 42 (28) |
| Logistic regression | 26 |
| Cox regression | 7 |
| Linear regression | 3 |
| LASSO (Logistic regression) | 1 |
| LASSO (Cox regression) | 1 |
| LASSO (model not specified) | 3 |
| Best subset regression with leave-out cross-validation | 1 |
| Non-regression-based models | 71 (47) |
| Neural network (including deep learning) | 18 |
| Classification tree (e.g., CART, decision tree) | 28 |
| Support vector machine | 12 |
| Naive Bayes | 6 |
| K nearest neighbours | 3 |
| Othera | 4 |
| Ensemble models | 39 (26) |
| Random forest (including random survival forest) | 23 |
| Gradient boosting machine | 8 |
| RUSBoost - boosted random forests | 1 |
| Bagging with J48 selected by Auto-WEKA | 1 |
| CoxBoost - boosted Cox regression | 1 |
| XGBoost: exTreme Gradient Boosting | 1 |
| Gradient boosting machine and Nystroem, combined using elastic net | 1 |
| Adaboost | 1 |
| Bagging, method not specified | 1 |
| Partitioning Around Medoid algorithm and complete linkage method | 1 |
| Median number of models developed per study [IQR], range | 2 [1–4], 1–6 |
CART Classification And Regression Tree, LASSO Least Absolute Shrinkage and Selection Operator
aOther includes voted perceptron; fuzzy logic, soft set theory and soft set computing; hierarchical clustering model based on the unsupervised learning for survival data using the distance matrix of survival curves; Bayes point machine