Artificial Neural Network for Multiclass Recognition and its Application to the Thyroid Functional State

Abstract

Background. Development of automated diagnostic requires selection and improvement of appropriate machine learning methods, in particular multiclass recognition. Artificial Neural Networks (ANN) of various architecture are considered as an approach to the problem. Results. Performance of the classifiers is compared in terms of accuracy, sensitivity, and specificity. Linear functional strategy classifier outperforms the other with more complex ANN-architecture and exhibits relative steadiness to overfitting. Performance of Kohonen neural network on large dataset exceeds 90 % in terms of specificity for each class, withal sensitivity for distinct classes is more than 95 %. Conclusions. The understanding of the strengths and limitations of each method is crucial for careful choice of ANNbased classifier, particularly its architecture, regularization and training algorithm.