Fast and effective assessment for individuals with special needs form optimization and prediction models

TL;DR

This study identifies key questions in psychological assessment forms that help classify children with special needs and develops models to make the process faster and more efficient.

Contribution

The study introduces optimized assessment forms and machine learning models for faster classification of individuals with special needs.

Findings

Certain items in the assessment form are more effective in classifying individuals into special needs categories.

Machine learning models were developed to predict diagnostic categories based on form items.

Reducing the number of questions can make the assessment process faster and more effective.

Abstract

The aim of this study was to determine which items in the psychological assessment forms used by counselling and research centres for individuals with special needs are effective in classifying individuals into special needs diagnostic categories. Data were obtained from the psychological assessment request forms of 1814 individuals aged 6 to 12 years who were referred to the centre between 2019 and 2023 with suspected special needs and who were classified as having special needs based on psychological and developmental assessments. In addition, we wanted to develop new predictive models using machine learning methods based on these items. Optimizing the psychological assessment application form so that it contains fewer questions may help to make the assessment process faster and more effective. It is expected that the results of this study will make an important contribution to saving time and energy for experts and individuals.

Figures