Leveraging Machine Learning in Data Analysis and Management for Special Education: Developing low-cost Digital Diagnostic and Media Therapeutic Approaches for Children with Neurodevelopmental Disorders in Impoverished Areas

Huixin Hu

doi:10.62051/es3s9412

Authors

Huixin Hu

DOI:

https://doi.org/10.62051/es3s9412

Keywords:

Neurodevelopmental Disorders; Machine Learning; Digital Diagnosis; Media Therapy; Poverty; ASD; ADHD.

Abstract

This study delves into the use of machine learning to develop cost-effective digital diagnostic and therapeutic tools for children with Neurodevelopmental Disorders (NDD) in underprivileged areas. Utilizing a predictive model, it engaged 300 students from low-income families in Nanfeng County, Jiangxi, China, evaluating their predisposition to Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD). The results underscore a heightened prevalence of these disorders in poverty-stricken settings relative to global norms. In addition the study demonstrates that digital methodologies for diagnosing and managing NDD are not only efficient and affordable but also practical for areas with limited resources. This approach crucially addresses the lack of NDD data and resources in such communities, offering vital insights into enhancing care and education for children with ADHD and ASD, as well as facilitating Epidemiological Data Collection and Management in economically challenged communities.

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