Cloud-Enabled ML Techniques for PTSD Assessment and Management

Accurate and scalable solutions are required for the assessment and management of Post-Traumatic Stress Disorder (PTSD). To improve PTSD diagnosis and treatment, this study explores combining cloud computing and machine learning (ML), especially Naive Bayes classifiers. The system enables enhanced accessibility, scalability, and real-time processing of data related to PTSD, including patient symptoms and treatment responses, by leveraging Cloud resources. Naive Bayes classification task performance is compared with traditional methods, indicating its suitability for implementation in healthcare settings. By leveraging computational resources and ML algorithms for improved decision support, this study addresses the shortcomings of conventional approaches and advances personalized PTSD care through creative Cloud-ML solutions. The implementation framework facilitates comprehensive PTSD management strategies by integrating data from various sources, training models on the cloud, and providing real-time analytics. The assessment criteria comprise specificity, sensitivity, and accuracy, confirming the resilience of the proposed system. To maximize treatment outcomes and patient well-being through advanced technological interventions for PTSD, such as diversifying datasets and adding more ML algorithms for comparative analysis. This work uses a cloud-enabled naive Bayes model to handle inaccurate PTSD evaluations for improving diagnosis accuracy, reducing evaluation time, and increasing remote accessibility for efficient PTSD treatment.