Table 5 Weighted average of classifiers for ASD dataset without missing values
From: Comparison of classification algorithms for predicting autistic spectrum disorder using WEKA modeler
Classifier | Training runtime (s) | Correctly classified instance % (Accuracy) | Incorrectly classified instance % | Kappa statistic | TP rate | FP rate | Precision (sensitivity) | Recall (specificity) | ROC | AUC |
|---|---|---|---|---|---|---|---|---|---|---|
Naïve Bayes | 0.03 | 98.9726 | 1.0274 | 0.9794 | 0.990 | 0.010 | 0.990 | 0.990 | 1.000 | 0.9997 |
Logistic regression | 0.11 | 95.2055 | 4.7945 | 0.9040 | 0.952 | 0.048 | 0.952 | 0.952 | 0.991 | 0.9893 |
KNN | 0.00 | 89.3836 | 10.6164 | 0.7878 | 0.894 | 0.105 | 0.895 | 0.894 | 0.897 | 0.8969 |
J48 | 0.01 | 100.0000 | 0.0000 | 1.0000 | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Random Forest | 0.27 | 100.0000 | 0.0000 | 1.0000 | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 |
DNN (2-layer) | 26.29 | 86.9863 | 13.0137 | 0.7393 | 0.870 | 0.131 | 0.870 | 0.870 | 0.928 | 0.9282 |
SVM | 0.80 | 100.0000 | 0.0000 | 1.000 | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 |