Advanced Predictive Modeling of Military PTSD: SCA-Guided LSTM Analysis

Military personnel frequently suffer from posttraumatic stress disorder (PTSD), a complicated mental health illness brought on by their exposure to terrible experiences during combat operations. For prompt assistance and intervention, it is essential to anticipate and comprehend the onset of PTSD. This study suggests a unique method for combining the LSTM network with the Sine Cosine Algorithm (SCA) for enhanced predictive modelling of military PostTraumatic Stress Disorder (PTSD). PTSD is a common mental health issue that affects military people, frequently stemming from their exposure to traumatic events that occurred during war. For PTSD to be effectively managed and treated, early recognition and intervention are essential. Due to the disorder’s complexity and dynamic nature, traditional predictive modelling tools frequently encounter difficulties in effectively predicting the onset of PTSD. The approach is a methodical procedure that starts with gathering data on PTSD from military members and ends with pre-processing the data using Z-score normalisation. The SCA method is then utilised to extract pertinent characteristics, which makes it possible to identify important patterns associated with PTSD. These features are utilized as input for a SCA-LSTM model, which combines the advantages of both algorithms to predict PTSD with accuracy. Experiments on a sizable dataset of military soldiers show that the suggested method is highly accurate in predicting the onset of PTSD with recall rates and precision. The increased prediction accuracy and robustness of the SCA-LSTM framework are demonstrated by comparative comparisons with baseline models. The performance and accuracy ($\mathbf{9 7 \%}$) of the prediction model are assessed by comprehensive model evaluation, guaranteeing its dependability in diagnosing PTSD patients. This research provides possible possibilities for improving mental health outcomes within this group by highlighting the need of utilising advanced predictive modelling approaches to improve early diagnosis and management of PTSD in military members.