Data and Scripts for 'Application of Multidimensional Cognitive Ability Tests in Computer-Assisted Diagnosis of Attention-Deficit/Hyperactivity Disorder and Its Subtypes in Children: Based on A Genetic Algorithm Optimized Back-Propagation Neural Network Predictive Model'

Accurately identifying and diagnosing whether a child has attention-deficit/hyperactivity disorder, as well as determining the specific subtype, are crucial for selecting and implementing targeted intervention and treatment strategies. However, previous artificial intelligence-based diagnostic models for attention-deficit/hyperactivity disorder have faced challenges in achieving a balance among cost-effectiveness, low operational threshold, and high predictive accuracy. The present study recruited 316 children aged 6 to 14 years, including 98 with the inattentive subtype, 80 with the combined subtype, and 138 typically developing controls. Using multidimensional cognitive ability tests and a back-propagation neural network optimized by a genetic algorithm, this study developed a computer-aided diagnostic model to distinguish children with attention-deficit/hyperactivity disorder and its specific subtypes. The model achieved an overall predictive accuracy of 90.82%, outperforming traditional logistic regression and support vector machine models. The cancellation test was identified as the strongest predictor for differentiating attention-deficit/hyperactivity disorder subtypes. Additionally, the cancellation test, digits forward test, paper folding test, and digits backward test were critical in reflecting the cognitive ability measured by that test was more reflective of the essential differences in cognitive deficits among attention-deficit/hyperactivity disorder subtypes. Specifically, the cancellation test and digits forward test positively predicted the inattentive subtype but negatively predicted the combined subtype. In contrast, the paper folding test and digits backward test negatively predicted the inattentive subtype while positively predicting combined subtype. Moreover, the predictive contribution of cognitive tests, including the cancellation test, paper folding test, digits forward test, visual search test, and go/no-go test, varied greatly across different genders and age groups in predicting attention-deficit/hyperactivity disorder subtypes. In conclusion, the artificial intelligence-based aided diagnostic model demonstrated robust predictive performance in identifying attention-deficit/hyperactivity disorder and its subtypes in children. These findings provide a strong foundation for developing artificial intelligence-driven expert diagnostic systems for attention-deficit/hyperactivity disorder in the future.