Predicting cessation of orthodontic treatments using a classification-based approach

Abstract

In recent years, dental care has received increasing attention from people across the globe. With growing living conditions, people are more aware of preventable conditions that might be avoided. Malocclusion is one among the most studied problems in orthodontics. The statistical predictive model building plays a vital role in dentistry particularly, for clinical decision making. Developing a model for predicting the factors affecting for discontinuation of treatment is a vital step in assessing the therapeutic effect of treatment, resource management and cost reduction in the healthcare industry. Logistic regression and Probit regression models are considered as a successful widely used approach to analyze a classification problem with factor predictor variables. In this study, Naïve Bayes classifier and random forest classification models are introduced to predict discontinuation of orthodontic treatments of dental patients. Based on this study the duration of active treatment was the most significant factor affecting the discontinuation of the treatment. When comparing the four approaches, random forest classifier showed the highest accuracy and specificity, while Naïve Bayes model indicated the highest sensitivity on the prediction of discontinuation of the treatment. Besides, the classification-based approach with modern predictive algorithms shows a robust result for orthodontic data.

A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration

“…These technologies have been harnessed to analyze radiographic images [23,[26][27][28][29][30][31][32][33], predict growth [24,34,35], optimize orthodontic treatment decision-making processes [13][14][15][16][17][19][20][21][22]25,36]. Regrettably, a limited number of studies have employed AI and ML methodologies in forecasting orthodontic treatment duration [37,38]. Within this subset, Dharmasena et al conducted a notable investigation utilizing two distinct ML algorithms, namely Naïve Bayes and Random Forest [37].…”

“…Regrettably, a limited number of studies have employed AI and ML methodologies in forecasting orthodontic treatment duration [37,38]. Within this subset, Dharmasena et al conducted a notable investigation utilizing two distinct ML algorithms, namely Naïve Bayes and Random Forest [37]. Their study focused on predicting the likelihood of either the continuation or discontinuation of orthodontic treatment, showcasing the potential of AI and ML techniques in this critical aspect of orthodontic practice.…”