“Constructing Machine Learning models for Orthodontic Treatment Planning: a comparison of different methods”

Abstract

Objective: In this study, we investigated the feasibility of leveraging different machine learning methods to help with clinical decision-making on orthodontic treatment planning, including the decision on extraction, extraction pattern and anchorage pattern.Setting and Sample Population: a group of 216 patients (156 extraction and 60 non-extraction) was enrolled.Materials and Methods: 32 input features were identified and used as the input variable. We proposed 7 machine learning methods including Logistic Regression, SVM, Decision Tree, Random Forest, Gaussian NB, KNN Classifier and Neural Network for extraction and 2 methods including Random forest and Neural Network for extraction and anchorage pattern.Results: For extraction decision, neural network yielded the most promising results by 93 % accuracy followed by Logistic regression (86 % accuracy and 93% precision), SVM and Random Forest (83 % accuracy and 90% precision). Naïve Bayesian classifier and KNN classifier failed to produce accuracy within the acceptable range but Naïve Bayes demonstrated the highest recall score with 92 %. For the decision on extraction & anchorage pattern neural network proved to be more reliable with 89 % accuracy on extraction pattern and 81% accuracy on extraction& anchorage pattern. Random Forest classifier with 41% accuracy did not show satisfactory results for this task. The most important features for decision on extraction in this study were Inter incisal angle, crowding in mandible, U1-FH, L1-NB and crowding in maxilla.Conclusion: The results demonstrate that neural network can yield considerable accuracy in a medical diagnosis model. However, other algorithms such as logistic regression and random forest can also be considered for simpler tasks such as extraction.