Supplementary Materials for ‘PGAE-ICA: A simplified digital system for intellectual measurement-assessment in children and adolescents using cognitive testing and machine learning techniques’.

The study aims to create an intelligence assessment system using primary cognitive ability tests across four domains and a machine learning model to differentiate between normal and abnormal intelligence. A total of 103 participants aged 9 to 17, were recruited for this study, including 39 with abnormal intelligence and 64 with normal intelligence. Participants completed the Chinese Wechsler Intelligence Scale for Children and primary cognitive ability tests in a counterbalanced order. Independent samples t-tests and partial correlation analyses were employed to validate whether the cognitive tests effectively reflected individual differences in intelligence and to examine the correlation between cognitive tests and intelligence, while excluding ineffective tests. A genetic algorithm-optimized extreme learning machine model was then constructed and trained to predict intellectual status of children and adolescents. The results indicated that after excluding the time selection task, the remaining eleven cognitive tests effectively reflected the differences between individuals with normal and abnormal intelligence, with significant positive correlations to intelligence. The optimized extreme learning machine model achieved a prediction accuracy of 92.63%, surpassing the unoptimized basic extreme learning machine, traditional logistic regression, and support vector machine models. Moreover, with increasing age, the predictive contributions of different cognitive tests for intelligence changes substantially. In summary, the validity of the digital and simplified intelligence measurement-assessment system for children and adolescents, named PGAE-ICA, developed in the present study has been confirmed, supporting its further application in clinical and scientific research fields.