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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Analysis and Prediction of Temporomandibular Joint Disorder Using Machine Learning Classification Algorithms
verfasst von : Roopa B. Kakkeri, D. S. Bormane
Erschienen in: Techno-Societal 2020
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Abstract
Temporomandibular joint disorder (TMD) includes specifically a series of musculoskeletal disorders that may affect the masticating system. Roughly 30–40 percent of adults today have oral problems, and the most common cause of oral problems is TMJ. This disorder is very prevalent in the general population, but it affects more women and young people. The focus of this research review was on the methods for detecting TMJ disorder using machine learning algorithms. Propelled with the rise in use of machine learning techniques in the research dimensions of medical diagnosis, in this paper there is an attempt to explore different classification for predicting the TMJ disorder. The proposed techniques are evaluated on real time TMJ datasets. Dataset related to TMJ screening in subjects had 84 instances and 11 attributes. After applying different machine learning techniques, results suggest that Naïve Bayes and Adaboost models work better with higher accuracy of 93% and 92%.