Abstract
Aims of the study: To predict the number of overhanging dental restorations (ODRs) using an artificial neural network (ANN) and determine the most important predictive variables.
Materials and methods: Patient- and restoration-related data were used as input variables to construct two networks, with (network 1) and without (network 2) the number of secondary caries lesions (SCLs) as input data. Output data were the number of ODRs. Of the 502 participants, data of the first 100 were used to build/train the model. Those of the remaining 402 were used to test the model for prediction accuracy.
Results: Model accuracy notably increased after training. Prediction of ODRs was more accurate in network 1. Allowing for an error of ±1, network 1 predicted the number of ODRs with an accuracy of 85.6%, whereas that of network 2 was only 82.1% accurate. The number of old fillings was the most important input variable, while gender was the least important.
Conclusion: Within the study limits, the ANN model predicted ODRs with more than 85% accuracy. The number of old fillings was the most important predictive variable