Table 3 Comparison of performance indices for Artificial Neural Network models when using new data sets for predicting in hospital mortality rate in patients with T2DM after major LEA in Spain
Indices | Description | Model 1 | Model 2 | Model 3 | Model 4 |
|---|---|---|---|---|---|
Accuracy rate | Ratio of instances correctly classified | 0.807 | 0.794 | 0.830 | 0.861 |
Error rate | Ratio of instances misclassified | 0.192 | 0.205 | 0.169 | 0.138 |
Sensitivity | Portion of actual positive which are predicted positive | 0.775 | 0.801 | 0.744 | 0.763 |
Specificity | Portion of actual negative predicted negative | 0.838 | 0.786 | 0.912 | 0.960 |
Precision | Portion of predicted positive which are actual positive | 0.821 | 0.787 | 0.888 | 0.951 |
Positive likelihood | Likelihood that a predicted positive is an actual positive | 4.794 | 3.756 | 8.476 | 19.23 |
Negative likelihood | Likelihood that a predicted negative is an actual negative | 3.733 | 3.962 | 3.569 | 4.06 |
F1 score | Harmonic mean of precision and sensitivity | 0.797 | 0.794 | 0.810 | 0.847 |
False positive rate | Portion of actual negative which are predicted positive | 0.161 | 0.213 | 0.087 | 0.039 |
False discovery rate | Portion of predicted positive which are actual negative | 0.178 | 0.212 | 0.111 | 0.048 |
False negative rate | Portion of actual positive which are predicted negative | 0.224 | 0.198 | 0.255 | 0.236 |
Negative predictive value | Portion of predicted negative which are actual negative | 0.795 | 0.800 | 0.790 | 0.800 |
Matthews correlation | Correlation between the targets and the outputs. It takes a value between -1 and +1 | 0.615 | 0.588 | 0.668 | 0.737 |
Informedness | Probability that the prediction method will make a correct decision as opposed to guessing | 0.613 | 0.588 | 0.656 | 0.723 |
Markedness | Probability of predicting the classifier labels from the real classes. | 0.659 | 0.574 | 0.801 | 0.911 |