A comparative study on machine learning techniques for prediction of success of dental implants

Authors:
Adriano Lorena Inácio Oliveira;Carolina Baldisserotto;Julio Baldisserotto
Affiliations:
Department of Computing Systems, Polytechnic School of Engineering, Pernambuco State University, Madalena, Recife, PE, Brazil;Department of Computing Systems, Polytechnic School of Engineering, Pernambuco State University, Madalena, Recife, PE, Brazil;Faculdade de Odontologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
Venue:
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

The market demand for dental implants is growing at a significant pace. In practice, some dental implants do not succeed. Important questions in this regard concern whether machine learning techniques could be used to predict whether an implant will be successful and which are the best techniques for this problem. This paper presents a comparative study on machine learning techniques for prediction of success of dental implants. The techniques compared here are: (a) constructive RBF neural networks (RBF-DDA), (b) support vector machines (SVM), (c) k nearest neighbors (kNN), and (d) a recently proposed technique, called NNSRM, which is based on kNN and the principle of structural risk minimization. We present a number of simulations using real-world data. The simulations were carried out using 10-fold cross-validation and the results show that the methods achieve comparable performance, yet NNSRM and RBF-DDA produced smaller classifiers.