# Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean Section
## 5 Discussion  
The subject of the present study was to present a model that can produce an early diagnosis to detect and alarm a possible case so that proper measures can be taken as soon as possible. According to our findings, emergency cesarean section, pathology of gestation, preterm birth, the inclusion of neonate in NICU, absence of breastfeeding, psychiatric history, expectations from childbirth, and support from the partner are included in the set of important decision factors.  
Additionally, as it can be seen from the results (graphs in Figures 4 and 5, Tables 11 and 12), the ability of the ANN model to arrive at a correct conclusion is demonstrated at a very satisfactory level (around 97% in training and 94% in testing) for the cases which are free of symptoms. For the cases that are PTSD diagnosed, the recognition level reaches 83% in training and 66% in testing. The area in between the above two categories has a low percentage of recognition and it collects the PTSD profile cases. As it can be observed from the results, the PTSD profile cases are the only ones that really need the late questionnaires data (after 6 weeks). According to the above, a policy that could be followed to arrive at a conclusion as soon as possible is to characterize a case that is not classified as free of symptoms as a possible PTSD case. If the case is indeed classified as PTSD, then such a scenario would probably denote an increased potentiality for the appearance of PTSD symptoms after six weeks when the second part of the data is collected. More focused treatment in such a case could be applied and this can start six weeks in advance, providing a beneficial period of medical care.  
The use of random decision forests for associating an importance value for each data field is very useful as well. The ordering of the early accessible variables according to their Gini values in Table 9 is the result of that process and it can be noted that this ordering is indeed profound. Criterion A, which constitutes a basic decision factor also in the typical DSM diagnosis, is ranked first and its related parts (A1 and A2) are just after that. Although there is one more datum field related to Criterion A, (v34, number of similar stressful experiences) we decided not to use this as it requires extra effort from the side of the woman in order to be defined. The rest of the data fields that are used for the datasets are all important and this can be shown by the gradual increase in PTSD sensitivity which is noticed in the training phase (Figure 4). This is expected and it denotes the usefulness of the extra information which is added to every dataset. This information increase is also depicted as the sums of the Gini values of the datasets in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Free** & **Profile** & **PTSD** & **Class. Error** & **Sd of Class.Error** \\ \hline
**Free** & **357.8** & 6.6 & 14.6 & 0.05594 & 0.00389 \\
**Profile** & 25.9 & **6.7** & 1.4 & 0.80294 & 0.03410 \\
**PTSD** & 21.6 & 0.9 & **33.5** & 0.40179 & 0.02560 \\ \hline \hline \end{tabular}
\end{table}
Table 13: The averaged confusion matrix and the classification errors from the RDF using the 46 variables remaining after removing the (20 + 4) ones directly related to the DSM-V. The complete dataset is used for the training.  
Figure 6: The graph of the sums of Gini importance values in the eight datasets (D1–D8) of Table 10.