Comparison efficiency of the artificial intelligence methods for the diagnosis of Acid - base and anion gap disorders

Abstract

Diagnosis of the most complicated disorders in acid-base status and accompanying electrolyte balance creates a lot of troubles for practicing physicians. The purpose of our study was to create and compare: 1) an artificial neural network, 2) genetic program, 3) fuzzy-neural system that can diagnose acid-base disorders, based on a set of laboratory gasometric and electrolyte measurements. We took into account 7 single acid-base disorders, 11 double acid-base disorders and 6 triple complicated disorders with accompanying anion gap alterations. We prepared a set laboratory measurements consisting of 250 results for training and the same number of results for testing the program. Finally, the efficiency of presented artificial intelligence (AI) methods has been described and compared.