Table 2 Performance comparison with previously published methods.
From: Predicting early Alzheimer’s with blood biomarkers and clinical features
Model | Classification | Inputs | Accuracy | AUC | Reference |
|---|---|---|---|---|---|
Random Forest with Chi-Square feature selection | Binary (CN, MCI/AD) | Gene expression and clinical data (no CS) | 0.65 | 0.65 | This work |
AdaBoost model with no feature selection | Binary (CN, MCI/AD) | SNPs and clinical data (no CS) | 0.67 | 0.63 | This work |
SVM model with MI feature selection | Binary (CN, MCI/AD) | SNPs and gene and clinical (with CS) | 0.95 | 0.94 | This work |
Deep neural network (DNN) | Binary (CN, AD) | Blood gene expression | NA | 0.656 | Lee and Lee22 |
SVM | Binary (CN, AD) | Blood gene expression | NA | 0.620 | Lee and Lee22 |
BSWiMS-LASSO-RPART ensemble | Binary (CN, AD) | SNPs | 0.677 | 0.719 | Oriol et al.25 |
Deep learning models (DL) | Binary (CN, MCI/AD) | SNPs | 0.66 | NA | Venugopalan et al.36 |
