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

Model 1: all discharge diagnosis included in the database. Model 2: all discharge diagnosis included in the database, except infectious diseases (ICD-9-CM codes 001 to 139). Model 3: comorbidities included in the Charlson Comorbidities Index. Model 4: comorbidities included in the Elixhauser Comorbidities Index