# Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean Section
## 4 Results  
### Initial Classification Using the ANN  
As mentioned above, the complete set of the data were used initially to examine the feasibility of the reproduction of the original classification according to the DSM-V \begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Number of Data Fields** & **Coded Labels** \\ \hline Criteria A, B, C, D, E & & \\ (binary variables) & 5 & v35, v36, v37, v38, v39 \\ \hline Answers to the twenty questions from DSM-V so that the PTSD & & \\ score and values of Criteria B, C, D, and E are defined (values in & 20 & v41–v60 \\ \{0,1,2,3,4\}. These answers and the corresponding values for Criteria B, C, D, and E are only available six weeks after the birth. & \\ \hline The third question related to Criterion A (A3), number of similar & & \\ stressful experiences. Min = 0, max = 11, median = 0.A & \\ Kruskal–Wallis H test showed that there was a statistically significant difference in its values between the three different & \\ diagnoses, H = 96.480, df = 2, \(p\) < 0.001, with a mean rank of & \\
219.32 for free, 249.71 for profile, and 332.31 for PTSD. & & \\ \hline The seventeen Life Events Checklist (LEC-5) of DSM-V. & & \\ Values are weighted and summed for each of the four severity & 17 & lec\_1–lec\_17 \\ options (personal, witness, other, and occupation related with & \\ weight 4.0, 3.0, 2.0 and 1.0, respectively) & & \\ \hline The total count of LEC-5 answers. Min = 0, max = 11, median = 1. & \\ A Kruskal–Wallis H test showed that there was a statistically significant difference in its values between the three different & 1 & v61 \\ diagnoses, H = 49.636, df = 2, \(p\) < 0.001, with a mean rank of & \\
214.89 for free, 341.76 for profile, and 306.25 for PTSD. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 1: A schematic diagram of the methodology used. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Free** & **Profile** & **PTSD** & **Precision** & **Recall (Sens.)** & **Specificity** \\ \hline
**Free** & **341.1** & 0 & 0 & 99.9\% & 100\% & 99.7\% \\
**Profile** & 0.2 & **28.8** & 1.5 & 100\% & 94.4\% & 100\% \\
**PTSD** & 0 & 0 & **50.4** & 97.1\% & 100\% & 99.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The averaged confusion matrix of the initial classification results for the training phase using the complete set of the 70 variables. The accuracy is 99.6%. From the 469 cases of the collected data, 379 (80.81%) were manually diagnosed as free of symptoms, 34 (7.24%) had traces and were characterized as profile and 56 (11.94%) were diagnosed as PTSD cases. For the training and testing phases, a stratified ten-fold cross-validation scheme was employed. The ANN was created using the PyTorch (v1.9.0 + cu11) library in Python and had a structure of seventy input units (in the case of the complete data fields as shown in Table 4), six hidden units, and three output units using three bits for the output where only one of them was set to \"1\" indicating the diagnosis (one hot coding). The connections were feed-forward from one layer to the next, the Sigmoid function (with \(\alpha=1.0\)) was used for activation and the mean squared error (MSE) was employed from the stochastic gradient descent (SGD) optimization algorithm for training. The learning rate was set to 1.0 and the momentum to 0.9. The tuning of the hyperparameters that were used was performed on a trial-and-error base after several initial experimentations. Initially, we estimated precision, recall, specificity, and accuracy for the complete set of the 70 variables by considering the confusion matrices and these are presented in Tables 5 and 6. Precision estimates how many positive predictions were correct. Recall estimates how many positives are correctly predicted while specificity estimates how many negatives are correctly predicted.