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Artificial neural network-based psychological assessment model for predicting the mental health problem in children facing psychological abuse and depression
Aggression and Violent Behavior  (IF4.874),  Pub Date : 2021-11-25, DOI: 10.1016/j.avb.2021.101711
Fang Rao, Wei Cao, Jianxue Huang, C. Sivapragash

Risk assessment is critical to prevent psychological abuse of children as it can classify high-risk situations requiring action to protect children. Despite the widespread use of risk evaluation tools in child care, the prediction of mental health issues and the properties of the tools are consistent with increased predictive efficacy are still unclear. The psychological abuse of children is associated with an increased risk of depression. However, depression is present in many people who receive abuse. Hence in this paper, Artificial Neural Network-based Psychological Symptom Prediction Model (ANN-PSM) has been proposed to reduce depression and improve children's psychological level. The ANN-PSM patterns can detect the children increasingly likely to create psychological abuse in the face of sad signals and an increased risk of depression. Findings help to explain the mechanisms by which psychiatric depression puts children at risk for socioemotional adaptation. The experimental findings indicate that ANN's can be accepted more generally in clinical decision-making and achieve the highest detection rate of 97.84% depression among children.