Prediction of attention deficit hyperactivity disorder based on explainable artificial intelligence.

Abstract

The aim of this study is to predict the probability being diagnosed with ADHD using ML algorithms and to explain the behavior of the model to support decision making. The dataset studied included 694 cases. Information was obtained on age, sex and WISC-IV scores. Algorithms belonging to different ML learning styles were tested. A stratified 10-fold-cross-validation was applied to evaluate the models. The metrics were used: accuracy, area under the receiver operating characteristic, sensitivity and specificity. We compared models using all initial features and a suitable wrapper-type feature selection algorithm. After, we calculated Shapley additive values to assign weights to each predictor based on its additive contribution to the outcome and explain the predictions. The Random Forest algorithm performed best on most metrics. The main predictors included, GAI-CPI, WMI, CPI, PSI, VCI, WMI - PSI, PRI and LN. The ML model adequately predicted ADHD diagnosis in 90% of cases.