Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram Data Article Swipe
Wenjun Xiang
,
Gang Zhu
,
Yiping Hou
,
Zhan‐Dong Mei
,
Lin Wan
,
Li Zhang
,
Guang Yang
,
Jian Zu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/tnsre.2025.3579763
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/tnsre.2025.3579763
Accurate diagnosis of Tic disorders (TD) and its severity based on electroencephalogram (EEG) data were of great clinical importance. This study analyzed EEG data from 90 children with TD and 88 healthy controls (HC). A two-stage progressive diagnosis framework based on EEG data and machine learning methods was developed. To achieve individualized prediction and reduce the feature dimension, we proposed a novel individual-based feature-weighted integration method in machine learning, as well as a new SHAP-driven feature selection and weighting (SFSW) strategy to improve the prediction accuracy. Based on 13 weighted features, Logistic Regression model achieved an average accuracy of 94.2% (95% CI, 90.6%-97.9%) in diagnosing TD, with a sensitivity of 92.4% (95% CI, 85.3%-99.5%) and a specificity of 96.1% (95% CI, 92.9%-99.2%). The Decision Tree model attained an average accuracy of 81.5% (95% CI, 68.6%-94.5%) in predicting severity, with a sensitivity of 81.5% (95% CI, 68.6%-94.5%) and a specificity of 89.9% (95% CI, 82.1%-97.6%). In the hold-out set validation, the method demonstrated accuracy rates of 95.7% in diagnosing TD and 83.3% in predicting severity. Interpretability analysis revealed that the top three main features affecting TD diagnosis were the mean frequency (MNF) of P3 channel $\beta $ band, age and MNF of C3 channel $\gamma $ band. This work offered a more efficient approach to individualized diagnosis of TD and had substantial practical value for clinical auxiliary diagnosis and intervention.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tnsre.2025.3579763
- OA Status
- diamond
- Cited By
- 1
- References
- 42
- Related Works
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- OpenAlex ID
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- OpenAlex ID
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https://openalex.org/W4411336719Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tnsre.2025.3579763Digital Object Identifier
- Title
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Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram DataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Wenjun Xiang, Gang Zhu, Yiping Hou, Zhan‐Dong Mei, Lin Wan, Li Zhang, Guang Yang, Jian ZuList of authors in order
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https://doi.org/10.1109/tnsre.2025.3579763Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/tnsre.2025.3579763Direct OA link when available
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Psychology, Electroencephalography, Artificial intelligence, Machine learning, Computer science, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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