English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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. Objective. The goal is to analyze and compare performance of ANN-based classifiers on various datasets for further improvement of model selection strategy. Methods. ANN-based models of the distribution of class labels in terms of predictor features are constructed, trained and validated for datasets of clinical records. Varying training algorithms for multi-layer perceptrons, Kohonen neural network, linear functional strategy with multi-parameters regularization are considered. 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 ANN-based classifier, particularly its architecture, regularization and training algorithm.

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