An Analysis of Thyroid Function Diagnosis Using Bayesian-Type and SOM-Type Neural Networks

Abstract

Both classification problems and QSAR analyses of drugs have been better studied by the multi-layer feedforward neural networks with back-propagation learning than the classical methods in pattern recognition such as linear multiple regression or adaptive least square method, since those problems often involve nonlinear relationships between descriptors and the class (/activity). [1][2][3] Recently Bayesian regularized neural networks (BRNN), [4][5][6][7] which extends back-propagation learning algorithm in order to overcome its defects such as the problems of local trapping, overfitting etc. by introducing probabilistic treatment of the Bayesian inference technique for the synaptic weights, has been successfully applied for QSAR studies 8,9) even for massive sample data. 10)Analytical chemistry is often concerned with the classification of samples into groups on the basis of chemical results while such classification process is called diagnosis if these groups are pathological cases. Zhang et al. studied classification problems of thyroid diagnosis using back-propagation neural networks and showed its usefulness. 11)Kohonen developed the unsupervised learning algorithm that simplified the mapping mechanism of the relatively homogeneous structures found in mammalian brains associated with the processing of sensory data, 12) and showed it possible to generate self-organizing map (SOM) of data. In the field of pharmaceutical sciences, the SOM was applied for searching useful drugs. 13-15)When we apply the method of neural networks to some problems encountered in medical and pharmaceutical fields, it is necessary to analyze each problem in a variety of viewpoints under various conditions. It is expected that appropriate disposal and total unified cognition of an individual problem would be possible through those accumulation.Thyroid Disease Diagnosis The correct diagnosis of thyroid dysfunctions based on clinical and laboratory tests is important work and neural networks would be helpful. Coomans et al. analyzed the thyroid data which takes five hormons related to thyroid and includes 215 patients divided into 3 classes as hyperthyroid, hypothyroid and normal by using the method of linear discriminant analysis. 16)Now, with radioimmunoassay techniques it is possible to measure hormones T3, T4 and TSH in the blood very accurately, and the onset of thyroid disease can be surely confirmed.17) It is recently usual to measure two laboratory tests TSH and free T4 in diagnosing thyroid diseases. Then applying neural networks to such case seems to be not necessary now. Another various applications of the notable neural networks for a diagnosis support, however, would be possible and then to make reanalysis of these laboratory tests data should be important.In the present work we make reanalysis of the thyroid data, which was once analyzed by Coomans et al., using the recent two notable neural networks. The BRNN has a powerful soft pruning method called automatic relevance determination (ARD) 5,6) in addition to the importa...

A comparative study of support vector machine and logistic regression for the diagnosis of thyroid dysfunction

“…The proposed framework has accomplished the most astounding classification accuracy revealed so far by 10-fold cross-validation (CV) technique, with the mean accuracy of 97.49% and with the greatest precision of 98.59%. In 2005, Kenji Hoshi et al [9] figured out the thyroid information by statistical technique, multivariate analysis and by two sorts of neural networks. One is the self-organizing map approach, that clusters the patients and shows outwardly a characteristic of the distribution according to laboratory tests.…”

Applications of Artificial Neural Networks in Chemical Problems

“…The drug design research involves the use of several experimental and computational strategies with different purposes, such as biological affinity, pharmacokinetic and toxicological studies, as well as quantitative structure-activity relationship (QSAR) models [87][88][89][90][91][92][93][94][95]. Another important approach to design new potential drugs is virtual screening (VS), which can maximize the effectiveness of rational drug development employing computational assays to classify or filter a compound database as potent drug candidates [96][97][98][99][100].…”

Assisting the Diagnosis of Thyroid Diseases with Bayesian-Type and SOM-Type Neural Networks Making Use of Routine Test Data

“…In the previous work 11) we analyzed the human thyroid data as the three class classification problem, that is, diagnosis by using two notable approaches, the Bayesian regularized neural networks and the self-organizing map. [12][13][14] The former presented the high classification rates and a nice soft pruning if ARD method is swichted on.…”

“…5,6,11) This is a multi-layer (three-layer) neural network, but was extended to the Bayesian probability framework of treating model parameters, to avoid defects like overfitting encountered in the traditional maximum likelihood approach. 4,5) The routine test parameters are assigned to each neuron of the input layer.…”