| Yazarlar (4) |
Nazım Tolgahan Yıldız
Karamanoğlu Mehmetbey Üniversitesi, Türkiye |
|
Hikmet Kocaman
Karamanoğlu Mehmetbey Üniversitesi, Türkiye |
|
Hasan Yıldırım
Türkiye |
Öğr. Gör. Mehmet CANLI
Kırşehir Ahi Evran Üniversitesi, Türkiye |
| Özet |
| 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 |
| clinical measurement | machine learning | prediction | temporomandibular disorders | variables |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | Medicine |
| Dergi ISSN | 1536-5964 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 10-2024 |
| Cilt No | 103 |
| Sayı | 41 |
| Sayfalar | 1 / 9 |
| Doi Numarası | 10.1097/MD.0000000000039912 |
| Makale Linki | https://doi.org/10.1097/md.0000000000039912 |
