RT Journal Article
T1 Prediction of attention deficit hyperactivity disorder based on explainable artificial intelligence.
A1 Navarro-Soría, Ignasi
A1 Ramón-Rico, Juan Ramón
A1 Juárez-Ruiz-de-Mier, Rocío
A1 Lavigne-Cerván, Rocío
K1 Trastornos por déficit de atención con hiperactividad
K1 Diagnóstico
K1 Inteligencia artificial - Aplicaciones médicas
AB 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.
PB Taylor & Francis
YR 2024
FD 2024-04-09
LK https://hdl.handle.net/10630/39208
UL https://hdl.handle.net/10630/39208
LA eng
NO Navarro-Soria, I., Rico-Juan, J. R., Juárez-Ruiz de Mier, R., & Lavigne-Cervan, R. (2024). Prediction of attention deficit hyperactivity disorder based on explainable artificial intelligence. Applied Neuropsychology: Child, 1-14.
NO https://openpolicyfinder.jisc.ac.uk/id/publication/21627
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026