Table 7 The results of the second experiment in terms of AAC for MLMs, using the PCA dimensionality reduction algorithm in different cases of pre-processing combination
From: Prediction of diabetes disease using an ensemble of machine learning multi-classifier models
Preprocessing | Algorithm | N | k-NN | SVM | DT | RF | GNB | AdaBoost | Best MLM |
|---|---|---|---|---|---|---|---|---|---|
I + N | PCA | 10 | \(0.754\pm 0.030\) | \(0.900\pm 0.001\) | \(0.934\pm 0.001\) | \(0.968\pm 0.003\) | \(0.962\pm 0.002\) | \(0.854\pm 0.001\) | RF |
PCA | \(11\) | \(0.757\pm 0.034\) | \(0.902\pm 0.002\) | \(0.953\pm 0.002\) | \(0.960\pm 0.003\) | \(0.966\pm 0.004\) | \(0.900\pm 0.002\) | RF | |
PCA | 12 | \(0.817\pm 0.008\) | \(0.908\pm 0.003\) | \(0.952\pm 0.001\) | \(0.964\pm 0.003\) | \(0.971\pm 0.001\) | \(0.914\pm 0.003\) | GNB | |
I + Z | PCA | 10 | \(0.826\pm 0.004\) | \(0.910\pm 0.005\) | \(0.820\pm 0.003\) | \(0.962\pm 0.003\) | \(0.974\pm 0.002\) | \(0.918\pm 0.001\) | GNB |
PCA | 11 | \(0.829\pm 0.001\) | \(0.914\pm 0.004\) | \(0.867\pm 0.004\) | \(0.970\pm 0.002\) | \(0.972\pm 0.002\) | \(0.920\pm 0.005\) | GNB | |
PCA | 12 | \(0.830\pm 0.0\) 20 | \(0.919\pm 0.006\) | \(0.901\pm 0.005\) | \(0.964\pm 0.001\) | \(0.977\pm 0.001\) | \(0.926\pm 0.006\) | GNB |