A study on the development and evaluation of a risk prediction model for childhood tic disorders
Objective To establish a risk prediction model for tic disorder(TD)in children in order to provide a basis for clinical diagnosis and treatment.Methods Initial screening of common risk factors for TD based on literature search and data mining was performed.Select 353 children aged 6-16 from the outpatient department of Liaoning University of Traditional Chinese Medicine Affiliated Hospital from December 2022 to July 2023,and divide them into two groups based on whether they had TD for risk factor investigation.Based on the findings,10 machine learning algorithms were used to build and compare TD occurrence risk prediction models:Decision Tree(DT),Linear Support Vector Machine(Linear SVC),Random Forest(RF),Linear Discriminant Analysis(LDA),Gradient Boosting,Bernoulli NB,Stochastic Gradient Descent(SGD),Ada Boost,XG Boost and Logical Regression(LR).Results There were statistically significant differences in risk factors of TD children compared to non-TD children(P＜0.05),including gender,emotional state,degree of learning difficulty,friendship,dietary type,preference for food habits,daily time spent watching electronic products,sleeping situation,recurrent respiratory infections,allergic diseases,history of febrile convulsions,brain diseases or traumatic brain injury,mother's age at pregnancy(≥35),adverse life history during pregnancy,intrauterine development disorders,family harmony,education and upbringing methods,and family history of mental illness.The optimal TD risk prediction model was the SGD model,with accuracy of 0.87,area under curve(AUC)of 0.918,sensitivity of 0.814,and specificity of 0.886.The top five risk factors contributing to the model were:educational methods,gender,emotional status,family history of mental illness and daily time spent watching electronic products.Conclusion The risk prediction model based on the SGD algorithm in this study is an optimal fitting prediction model,which has some predictive value and provides a basis for clinical diagnosis and treatment.
tic disorderrisk factorscharacteristic analysisprediction model
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