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.