Post-Traumatic Stress Disorder (PTSD) Analysis using Machine Learning Techniques

The presence of Post-Traumatic Stress Disorder (PTSD) is a significant psychiatric condition that can manifest after experiencing a traumatic event. To ensure effective treatment, the accurate diagnosis of PTSD is crucial. One test that can be used to measure brain activity is Electroencephalogram (EEG), which involves placing electrodes on the scalp. Machine learning (ML) techniques can be employed to analyze large datasets obtained from EEG to enhance PTSD diagnosis. This research project use ML algorithms to correctly classify PTSD through EEG data. The ML models will be trained using features from various EEG frequency bands to differentiate between individuals with and without PTSD. In addition, further clinical data will be studied to improve the accuracy of classification. The results of this research will include the creation of a trustworthy ML-based system that can diagnose PTSD using EEG data. This system will improve patient care, reduce errors, and aid in treatment decision-making. The effectiveness of the diagnosis will be validated through real-time medical records and consideration of treatment context for better outcomes.