Improvement of the classification quality in detection of Hashimoto's disease with a combined classifier approach.

The purpose of the study was to construct an efficient classifier that, along with a given reduced set of discriminant features, could be used as a part of the computer system in automatic identification and classification of ultrasound images of the thyroid gland, which is aimed to detect cases affected by Hashimoto's thyroiditis. A total of 10 supervised learning techniques and a majority vote for the combined classifier were used. Two models were proposed as a result of the classifier's construction. The first one is based on the K-nearest neighbours method (for K = 7). It uses three discriminant features and affords sensitivity equal to 88.1%, specificity of 66.7% and classification error at a level of 21.8%. The second model is a combined classifier, which was constructed using three-component classifiers. They are based on the K-nearest neighbours method (for K = 7), linear discriminant analysis and a boosting algorithm. The combined classifier is based on 48 discriminant features. It allows to achieve the classification sensitivity equal to 88.1%, specificity of 69.4% and classification error at a level of 20.5%. The combined classifier allows to improve the classification quality compared to the single model. The models, built as a part of the automatic computer system, may support the physician, especially in first-contact hospitals, in diagnosis of cases that are difficult to recognise based on ultrasound images. The high sensitivity of constructed classification models indicates high detection accuracy of the sick cases, and this is beneficial to the patients from a medical point of view.