The use of decision tree induction and artificial neural networks for automatic diagnosis of Hashimoto’s disease

• We have presented 2 models for classification of ultrasound thyroid images.
• Decision tree induction and multilayer perceptron neural network methods have been used.
• Used methods let to achieve the classification accuracy of sick cases at the level of 89.41%.
• Models may detect the cases affected by early stage of Hashimoto’s disease.
• Models can be used in an advisory system for support a doctor in the diagnosis of Hashimoto’s disease.

Methods for classification of ultrasound thyroid images have been presented. These methods allow us to classify examined patients as either sick or healthy. Decision tree induction and a multilayer perceptron neural network have been used to build classification models. Test results showed that the proposed methods can provide a starting point for building a support system in the process of medical diagnosis. Better accuracy of classifiers was achieved for the normalized images. We have also found that, under adopted assumptions, the results obtained for them were statistically significant in contrast to original images. The proposed methods allow us to separate a fairly large group of incorrectly classified cases. According to the authors, this group may contain features of the early stage of Hashimoto’s disease.