Risk assessment models of tic disorders with comorbid attention deficit hyperactivity disorder based on machine learning

LIU Fang; WANG Huaqian; NIU Ping; YAO Baozhen; YE Jingping; WANG Gaohua

doi:10.14188/j.1671-8852.2024.0237

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Risk assessment models of tic disorders with comorbid attention deficit hyperactivity disorder based on machine learning

更新时间：2026-11-05

- Risk assessment models of tic disorders with comorbid attention deficit hyperactivity disorder based on machine learning
- Medical Journal of Wuhan University Vol. 46, Issue 10, Pages: 1247-1253(2025)
- 作者机构：
  
  1.武汉大学人民医院儿科湖北武汉 430060
  2.武汉大学人民医院精神科湖北武汉 430060
- 作者简介：
- 基金信息：
- DOI：10.14188/j.1671-8852.2024.0237
  CLC： R749
- Received：08 April 2024，
  
  Published：25 October 2025
- 稿件说明：
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刘芳,王华倩,牛萍等.基于机器学习的抽动障碍共患注意缺陷与多动障碍的风险评估模型研究[J].武汉大学学报（医学版）,2025,46(10):1247-1253.

LIU Fang,WANG Huaqian,NIU Ping,et al.Risk assessment models of tic disorders with comorbid attention deficit hyperactivity disorder based on machine learning[J].Medical Journal of Wuhan University,2025,46(10):1247-1253.
刘芳,王华倩,牛萍等.基于机器学习的抽动障碍共患注意缺陷与多动障碍的风险评估模型研究[J].武汉大学学报（医学版）,2025,46(10):1247-1253. DOI： 10.14188/j.1671-8852.2024.0237.

LIU Fang,WANG Huaqian,NIU Ping,et al.Risk assessment models of tic disorders with comorbid attention deficit hyperactivity disorder based on machine learning[J].Medical Journal of Wuhan University,2025,46(10):1247-1253. DOI： 10.14188/j.1671-8852.2024.0237.

摘要

目的

基于机器学习方法构建抽动障碍（TD）共患注意缺陷与多动障碍（ADHD）的风险评估模型及筛选风险因素。

方法

调查收集363例TD患儿的临床及人口学和家庭环境信息作为研究数据。采用准确率、精确率、召回率/灵敏度、特异度、F1值及ROC曲线下面积（

AUC

），比较基于Logistic回归、朴素贝叶斯（NB）、随机森林（RF）、K最近邻（KNN）分类算法和支持向量机（SVM）5种机器学习方法的TD共患ADHD风险评估模型的性能。采用RF算法进行特征重要性排序和采用多因素Logistic回归分析筛选风险因素。

结果

在准确率指标上，NB模型表现最优；在F1值、召回率/灵敏度指标上，Logistic回归模型表现均最优；在精确率、特异度指标上，SVM模型表现均最优；NB、RF、Logistic回归模型的ROC曲线

AUC

较大（分别为0.896、0.891、0.882）。RF算法显示TD病情严重程度、家庭教养方式、合并行为问题为特征重要性排序前三位的因素，多因素Logistic回归分析显示TD病情中重度（

=25.92，95%

：12.18~55.13）、合并行为问题（

=3.72，95%

：1.89~7.33）、放纵型家庭教养方式（

=2.59，95%

：1.05~6.40）是TD共患ADHD的危险因素。

结论

机器学习模型可用于TD共患ADHD的风险评估。TD病情严重程度、合并行为问题、家庭教养方式是TD共患ADHD的显著风险因素。

Abstract

Objective

To develop risk assessment models for tic disorders (TD) with comorbid attention deficit hyperactivity disorder (ADHD) and to screen for risk factors using machine learning.

Methods

The clinical

demographic

and family environmental information of 363 children with TD were investigated and collected as study data. The performance of five machine learning models of TD with comorbid ADHD based on logistic regression

Naive Bayes (NB)

Random Forest (RF)

K‑Nearest Neighbor (KNN) classification algorithm

and Support Vector Machine (SVM) was compared using metrics such as accuracy

precision

recall/sensitivity

specificity

F1‑score

and area under the curve (

AUC

) of ROC. Feature importance was ranked using the RF algorithm

and multivariate logistic regression analysis was conducted to identify risk factors.

Results

The NB model demonstrated the highest accuracy; the logistic regression model performed best in terms of F1‑score and recall/

sensitivity; the SVM model achieved the highest precision and specificity.The

AUC

values of the NB

and logistic regression models were relatively large (0.896

0.891

and 0.882

respectively). Multivariate logistic regression analysis showed that moderate/severe tic severity (

=25.92

95%

: 12.18⁃55.13)

behavioral problems (

=3.72

95%

: 1.89⁃7.33)

and indulgent parenting pattern (

=2.59

95%

: 1.05⁃6.40) were risk factors for TD with comorbid ADHD.

Conclusion

Machine learning models can be effectively used for risk assessment of TD with comorbid ADHD. Tic severity

comorbid behavioral problems

and parenting pattern were identified as significant risk factors.

关键词

Keywords

references

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