Cloud-Powered SVM Solutions for Automated Monitoring and Intervention of ADHD Symptoms

Diagnosing and managing attention deficit hyperactivity disorder (ADHD) are very challenging tasks. Automated symptom monitoring and intervention for ADHD is the focus of this project, which investigates the use of Support Vector Machine (SVM) algorithms hosted on the cloud. The SVM models are trained to identify patterns relevant to ADHD symptoms using data collected from wearable devices and digital self-reports. Due to the cloud’s architecture, data can be processed and analyzed in real-time, allowing prompt actions. With a sensitivity of $85 \%$ and a specificity of $92 \%$, the results show that the SVM models accomplish a high level of accuracy in symptom identification. The system provides personalized treatments, such as medication adherence reminders and mindfulness exercises, via continuous monitoring and are customized to individual symptom profiles. Longitudinal studies can show how symptoms develop and how well treatments work overtime. Improving ADHD treatment via providing timely interventions and personalized assistance is the goal of this cloudpowered SVM system. The algorithms will be fine-tuned, more data sources will be included, and clinical trials will be conducted to evaluate the system’s performance in real-world scenarios.