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Predicting temporomandibular disorders in adults using interpretable machine learning methods: a model development
Yuchen Cui1, Fujia Kang1, Xinpeng Li1
1Department of Orthodontic, Hospital of Stomatology, Jilin University, Changchun, Jilin Province, China.
Frontiers in Bioengineering and Biotechnology
|November 20, 2024
Summary
Machine learning identified key risk factors for temporomandibular disorders (TMD) in adults. An interpretable model predicts TMD risk, aiding clinical assessment and disease management.
Area of Science:
- Oral health research
- Biomedical informatics
- Machine learning applications in healthcare
Background:
- Temporomandibular disorders (TMD) are prevalent with complex causes.
- Accurate risk prediction for TMD is crucial for effective management.
Purpose of the Study:
- To identify risk factors for TMD in adults using machine learning.
- To develop and validate an interpretable predictive model for TMD risk.
Main Methods:
- Utilized 5 machine learning algorithms on data from 949 adults.
- Employed feature importance and selection methods.
- Evaluated models using AUC, PR curves, calibration, and decision curve analysis.
Main Results:
- A Random Forest (RF) model demonstrated superior performance.
- An interpretable RF model identified 7 key risk factors: gender, malocclusion, unilateral chewing, chewing hard substances, teeth grinding, teeth clenching, and anxiety.
- The model achieved high predictive accuracy (AUCs: 0.892 training, 0.854 internal validation, 0.857 external test).
Conclusions:
- Developed an efficient and interpretable machine learning model for adult TMD risk prediction.
- The model offers high accuracy and clinical utility, validated by SHAP analysis.
- Provides clinicians with a practical tool for TMD risk assessment and management.
