Application of Kohonen neural networks for the non-morphological distinction between glomerular and tubular renal disease.

Abstract

A Kohonen topological map is an artificial intelligence system of the connectionist school (neural networks). The map learns the typical features of the subclasses in the learning set by means of a shortest Euclidean distance algorithm, after which self-adaptation of the neurons occurs. By its ability of self-organization and generalization, a Kohonen map is useful for pattern recognition, and its application in the medical field as an aid for decision making seems promising. This study describes the use of a Kohonen topological mapping system in the classification of renal diseases as being glomerular or tubular on basis of clinical characteristics and laboratory results. Forty-one parameters from 75 patients were retrospectively retrieved and used to train four different Kohonen maps of 10 x 10 neurons. For reference diagnostic classification, we referred to the results of the light-microscopic examination. The classification of the patients by the four different Kohonen networks was compared to the classification by a rule-based system and by three nephrologists. We also developed a 'hybrid' decision system that makes a classification on basis of the opinion of the four networks and that of the rule-based system. The results show that a Kohonen map is capable of classifying the patients as having glomerular or tubular disease with a higher sensitivity and predictive value than the nephrologists and the rule-based system, and that the best classification was performed by the hybrid system: sensitivity and predictive value for the diagnosis 'glomerular' respectively 100 and 88% for the network with the most adequate results, 90 and 83% for the nephrologists, 90 and 95% for the rule-based system, and 95 and 96% for the hybrid system; sensitivity and predictive value for the diagnosis 'tubular' respectively 50 and 100% for the neural networks, 31 and 45% for the nephrologists, 81 and 68% for the rule-based system, and 87 and 82% for the hybrid system). We conclude that a Kohonen map is capable of classifying the patients as having glomerular or tubular disease with a high sensitivity and predictive value. The rule-based system performs worse than the neural networks. The most adequate results were obtained with the hybrid system.