# Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean Section
## 1 Introduction  
Post-traumatic stress disorder (PTSD) is a mental health problem that can develop after a person goes through a life-threatening event. The disorder can develop even when the person is witnessing an event, exposed through information, or extreme repeated exposure to the workplace [1]. The disorder, regardless of the type of exposure to trauma, causes symptoms of re-experiencing, avoidance, negative cognitions in the mood, and arousal. The duration of symptoms lasts more than a month, not due to the action of any substanceor physical condition and causes a significant reduction in the individual's social life [2]. Anyone can develop PTSD at any age. Women, however, are twice as likely to develop PTSD as men, showing how they are most affected by traumatic childbirth experiences, hormonal disorders, stressful life events, and domestic violence [3].  
On the other hand, PTSD profile, or partial PTSD, originally used in relation to Vietnam veterans has recently been extended to trauma victims. The PTSD profile includes the most important symptoms of PTSD, but people exposed to trauma do not meet all the diagnostic criteria of the disorder. A correlation has also been found between PTSD profiles with increased rates of suicidal ideation, alcoholism, overuse of health services, and several absences from the work environment as well as a negative reduction of a person's social life [4; 5].  
For several years, scientists viewed the childbirth experience as a positive experience, regardless of the presence of traumatic events. In recent years, however, birth trauma has increased researchers' interest, as it has been shown that it can develop into PTSD or PTSD profile. Actually, more than 1/3 of mothers experienced their delivery as a traumatic event, while 1/4 of them will experience postpartum PTSD [6]. Some factors can increase the chance that a postpartum mother will have PTSD, such as pathology of gestation, complicated vaginal delivery, personal history of mental disorders, tokophobia, low social support, past PTSD, and cesarean section (CS) [7; 8; 9; 10]. Postpartum PTSD symptoms are debilitating and affect the social, professional, psychological, and communication function of the mother-infant bond and her family, as well [10]. However, there are many previous and current surveys that highlight the effect of CS on maternal mental health, especially emergency cesarean section (EMCS) which show a strong correlation with postpartum PTSD compared to other types of births [11; 12; 13; 14; 15; 16].  
Due to the nature of the current diagnosis procedure, which is in accordance with the (DSM-V), in order to reach a conclusion, it is necessary to wait for a period of six weeks to fill up the necessary questionnaires regarding any symptoms. However, the early detection of the possibility of developing PTSD could offer medical personnel significant information to take increased precautionary measures and alleviate any symptoms in advance.  
This observation is behind the motivation of the present work. More specifically, our motivation is to examine if machine learning and especially the artificial neural network (ANNs) models can be applied to predict possible PTSD cases. Our contribution is the development of an ANN model that can detect PTSD cases using a minimum amount of information and produce an early PTSD diagnosis as soon as possible.  
The rest of the paper is organized as follows: Section 2 presents the related work. In Section 3, the dataset and the proposed methodology for early diagnosis of PTSD cases are described in detail. Section 4 presents the experimental study which is based on a dataset with 469 cases. Section 5 discusses the results while Section 6 concludes the paper and gives directions for future work.