An investigation of machine learning algorithms for prediction of temporomandibular disorders by using clinical parameters

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dc.contributor.authorYıldız, Nazım Tolgahan
dc.contributor.authorKocaman, Hikmet
dc.contributor.authorYıldırım, Hasan
dc.contributor.authorCanlı, Mehmet
dc.date.accessioned2025-01-12T17:19:54Z
dc.date.available2025-01-12T17:19:54Z
dc.date.issued2024
dc.departmentKMÜ, Kamil Özdağ Fen Fakültesi, Matematik Bölümü
dc.departmentKMÜ, Sağlık Bilimleri Fakültesi, Fizyoterapi ve Rehabilitasyon Bölümü
dc.description.abstractThis 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 Adaptive Regression Spline (MARS) algorithm (accuracy = 0.8966, area under receiver operating characteristic curve = 0.9387, F1-score = 0.9032) was the best performing model regarding the performance criteria. According to this model, the 5 most important variables for predicting TMD were pain intensity, MMO, lateral excursion and PPT values of masseter and temporalis anterior muscles, respectively. The Bagging algorithm using the MARS algorithm is a robust model that, in combination with clinical parameters, assists in the detection of patients with TMD in settings with limited capabilities. The clinical parameters and ML algorithm proposed in this study may assist clinicians inexperienced in TMD to make a preliminary detection of TMD in clinics where diagnostic imaging tools are limited.
dc.identifier.doi10.1097/MD.0000000000039912
dc.identifier.issn0025-7974
dc.identifier.issn1536-5964
dc.identifier.issue41
dc.identifier.pmid39465879
dc.identifier.scopus2-s2.0-85206976569
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1097/MD.0000000000039912
dc.identifier.urihttps://hdl.handle.net/11492/10260
dc.identifier.volume103
dc.identifier.wosWOS:001338585900018
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorYıldız, Nazım Tolgahan
dc.institutionauthorKocaman, Hikmet
dc.institutionauthorYıldırım, Hasan
dc.institutionauthoridYıldız, Nazım Tolgahan/0000-0002-2404-2884
dc.institutionauthoridKocaman, Hikmet/0000-0001-5971-7274
dc.institutionauthoridYıldırım, Hasan/0000-0003-4582-9018
dc.language.isoen
dc.publisherLippincott Williams & Wilkins
dc.relation.ispartofMedicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectClinical Measurement
dc.subjectMachine Learning
dc.subjectPrediction
dc.subjectTemporomandibular Disorders
dc.subjectVariables
dc.titleAn investigation of machine learning algorithms for prediction of temporomandibular disorders by using clinical parameters
dc.typeArticle

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