Differential Diagnosis of Erythemato-Squamous Diseases Using Ensemble of Decision Trees
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The differential diagnosis of erythemato-squamous diseases (ESD) in dermatology is a difficult task because of the overlapping of their signs and symptoms. Automatic detection of ESD can be useful to support physicians in making decisions if the model gives comprehensible explanations and conclusions. Several approaches have been proposed to automatically diagnosis ESD, including artificial neural networks (ANN) and support vector machines (SVM). Although, these methods achieve high performance accuracy, they are not attractive for dermatologists because their models are not directly usable. Decision trees can be converted into a set of if-then rules, which makes them particularly suitable for rule-based systems. They have been already used for the diagnosis of ESD. In this paper, we investigate the performance of boosting decision trees as an ensemble strategy for the diagnosis of ESD. We consider two decision tree models, namely unpruned decision tree and pruned decision tree. The experimental results obtained on UCI dermatology data set show that boosting decision trees leads to a relative increase in accuracy that attains 5.35%. Comparison results with other related methods demonstrate the competitiveness of the ensemble of unpruned decision trees. It performs 96.72% accuracy, which is better than those of some methods, such as genetic algorithms and K-means clustering.
KeywordsSupport Vector Machine Decision Tree Association Rule Feature Selection Method Lichen Planus
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