Comparison between machine learning algorithms in tooth extraction in orthodontics

Yi Li
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Yi Li: Brigham and Women's Hospital

No k4xh7, OSF Preprints from Center for Open Science

Abstract: This paper is my master's thesis. The paper aims to apply machine learning algorithms to predict the outcome of decision making at tooth extraction in the modern era of Orthodontics practice. Section 1 provides an introduction to background of tooth extraction in Orthodontics and an overview of UNC dataset. Section 2 focuses on literature review for prediction evaluations, decision boundary, and five mainstream machine learning algorithms that include logistic regres- sion, stochastic gradient decent (SGD), random forest, multilayer perceptron (MLP) and convolutional neural network (CNN). Section 3 provides the analysis results based on the aforementioned predictive models. Limitations and possible adaptions of each modeling strategy are discussed in Section 4.

Date: 2018-04-13
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:k4xh7

DOI: 10.31219/osf.io/k4xh7

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