ARTIFICIAL NEURAL NETWORKS IN DIAGNOSIS OF THYROID-FUNCTION FROM IN-VITRO LABORATORY TESTS

We studied the potential benefit of using artificial neural networks ( ANNs) for the diagnosis of thyroid function. We examined two types of ANN architecture and assessed their robustness in the face of diagnost ic noise. The thyroid function data we used had previously been studie d by multivariate statistical methods and a variety of pattern-recogni tion techniques. The total data set comprised 392 cases that had been classified according to both thyroid function and 19 clinical categori es. All cases had a complete set of results of six laboratory tests (t otal thyroxine, free thyroxine, triiodothyronine, triiodothyronine upt ake test, thyrotropin, and thyroxine-binding globulin). This data set was divided into subsets used for training the networks and for testin g their performance; the test subsets contained various proportions of cases with diagnostic noise to mimic real-life diagnostic situations. The networks studied were a multilayer perceptron trained by back-pro pagation, and a learning vector quantization network. The training dat a subsets were selected according to two strategies: either training d ata based on cases with extreme values for the laboratory tests with r andomly selected nonextreme cases added, or training cases from very p ure functional groups. Both network architectures were efficient irres pective of the type of training data. The correct allocation of cases in test data subsets was 96.4-99.7% when extreme values were used for training and 92.7-98.8% when only pure cases were used.