Machine learning techniques for the evaluation of external skeletal fixation structures
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In this thesis we compare several machine learning techniques for evaluating external skeletal fixation proposals. We experimented in the context of dog bone fractures but the potential applications are numerous. Decision trees tend to give both binary and multiple-class predictions quickly and accurately. The classifier system method does worse due to the small size of the data set and missing values. The use of Artificial Neural Networks is promising, although it takes considerable time in training. A Genetic Algorithm is also employed to find the best parameters of the Neural Network. Experimental results for the different methods are presented and compared.