A Comprehensive Monitoring System for the Assessment of Mental Disorders in Children and Adolescents Utilizing Fuzzy Logic Algorithms and Machine Learning Techniques

The increasing prevalence of mental disorders among children and adolescents demands the development of innovative monitoring systems. This paper presents a novel approach for early detection and intervention in mental health issues using digital behavior analysis. By integrating fuzzy logic and machine learning algorithms, the proposed system can effectively identify early signs of mental health disturbances, such as depression and anxiety, through behavioral indicators collected from digital platforms. The system leverages digital footprints, such as social media interactions, messaging patterns, and online activity, to provide a comprehensive analysis of a child or adolescent’s emotional and psychological state. The results suggest that combining fuzzy logic for ambiguous data handling with machine learning for predictive modeling offers a promising solution for mental health monitoring. This approach not only facilitates timely interventions but also provides personalized support for individuals based on their digital behavior.

کلیدواژه ها:

fuzzy logic ، Machine Learning ، AI ، Mental Disorders in Children