Using fuzzy decision support to create a positive mental health environment for preschoolers.

The preschool period is a crucial time for behavioural and social-emotional development and the cultivation of mental well-being. Preschoolers may be affected by various traumatic problems. During this process, preschoolers may develop hazardous behaviours such as defiance, aggression, speech delays, difficulty socializing, and emotional dysregulation. To assess their mental health before starting school, preschoolers need early detection, intervention, and assessment. However, data shortages, heterogeneity, privacy issues, model interpretability, and generalization restrictions hamper the review process. This study sought to improve toddlers' behaviour by creating an effective decision-making mechanism. This study uses a fuzzy decision support (FDS) system using fuzzy rules and a degree of membership function to overcome the obstacles. Fuzzified data from the Preschool Pediatric Symptom Checklist (PPSC) was utilized to study preschoolers' behavior. Follow guidelines to decrease uncertainty to get a fuzzy set value. Afterwards, de-fuzzification was done according to the membership level needed to make effective mental health decisions. The FDS process identifies the relationship between a child's behaviour and attention level with maximum accuracy (97.98%), specificity (96.79%), sensitivity (97.08%), and minimum error (0.28). Behavioural prediction helps improve preschoolers' mental health and activities effectively. The system's excellence was analyzed using different metrics, ensuring 96.79% specificity and 97.98% accuracy. The dataset used in this study may lack sufficient diversity, limiting the generalizability of the findings across different socio-economic, cultural, and demographic groups. Future work should explore integrating real-time data collection methods like wearable devices or mobile applications to gather more comprehensive and dynamic behavioural data.