Risk prediction of attention deficit hyperactivity disorder based on machine learning
Objective To explore the feasibility of predicting attention deficit hyperactivity disorder(ADHD)in children based on machine learning algorithm.Methods A total of 358 patients treated in the pediatric outpatient department of our hospital from November 2022 to August 2023 were retrospectively analyzed,and 119 patients were finally included in the ADHD group and 239 patients in the non-ADHD group.Totally 31 variables,including basic demographic information,children's personal life situation,mother's pregnancy situation,family life situation and genetic factors,were taken as risk factors.Single factor analysis was used to select variables with obvious differences,and then the decision tree(DT)model,random forest(RF)model,adaptive enhancement algorithm(Adaboost)and K-nearest neighbor algorithm(KNN)models were established respectively.AUC,specificity,accuracy,F1 score and ROC curve were used to evaluate the model prediction efficiency.Results Random forest algorithm was the best predictive model for ADHD,with AUC being 0.955,and specificity,accuracy and F1 scores being 0.903,0.898 and 0.853,respectively.Meanwhile,the top five characteristic variables screened according to the random forest model were:education style,emotional stability,daily time spent playing with electronic products,learning difficulties,and recent recurrent respiratory infections.Conclusion A prediction model of child ADHD based on machine learning algorithm is established,which has good prediction ability for ADHD.
万方数据
维普
