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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 if an implant will be successful and which are the best techniques for this problem. This paper presents a comparative study on three machine learning techniques for prediction of success of dental implants. The techniques compared here are: (a) support vector machines (SVM); (b) weighted support vector machines; and (c) constructive RBF neural networks (RBF-DDA) with parameter selection. 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 RBF-DDA had the advantage of building smaller classifiers.

A Comparative Study on Support Vector Machine and Constructive RBF Neural Network for Prediction of Success of Dental Implants