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| This study aimed to predict temporomandibular disorder (TMD) using machine learning (ML) approaches based on measurement parameters that are practically acquired in clinical settings. 125 patients with TMD and 103 individuals without TMD were included in the study. Pain intensity (with visual analog scale), maximum mouth opening (MMO) and lateral excursion movements (with millimeter ruler), cervical range of motion (with goniometer), pressure pain threshold (PPT; with algometer), oral parafunctional behaviors (with Oral Behaviors Checklist), psychological status (with Hospital Anxiety and Depression Scale), and quality of life (with Oral Health Impact Profile) were evaluated. The measurements were analyzed via over 20 ML algorithms, taking into account an extensive parameter tuning and cross-validation process. Results of variable importance were also provided. Bagging algorithm using Multivariate ... |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SCOPUS dergilerinde yayımlanan tam makale |
| Dergi Adı | Medicine |
| Dergi Tarandığı Indeksler | |
| Makale Dili | İngilizce |
| Basım Tarihi | 10-2024 |
| Cilt No | 103 |
| Sayı | 41 |
