In Australia, the prevalence and severity of mental illness in children and adolescents is on the rise. It is a major issue that has a negative impact on productivity, self-esteem, and relationships of the young generation. To date, little attention in the literature has been paid to the influential determinants of prevalent mental illness. The key objective of this study is to implement a machine learning (ML) model to detect the significant factors for mental illness, that contribute to concerns in children's mental health and development from an Australian perspective. This predictive analysis model will reveal the key factors that influence the prevalence of mental illness among Australian children and adolescents. It will examine the sign and symptoms, family activities and socio-economic issues which are responsible for mental illness. This study will also investigate the relation between data by identifying the association between the issues that the children or adolescents do not want to disclose.
For more information, please contact the Graduate Research School.