Optimization of supervised self-organizing maps with genetic algorithms for classification of urinary calculi

Supervised self-organizing maps were used for classification of 160 infrared spectra of urinary calculi composed of calcium oxalates (whewellite and weddellite), pure or in binary or ternary mixtures with carbonate apatite, struvite or uric acid. The study was focused to such calculi since more than 80% of the samples analyzed contained some or all of the above-mentioned constituents. The classification was done on the basis of the infrared spectra in the 1450–450 cm−1 region. Two procedures were used in order to find the most suitable size and for optimizing the self-organizing map of which that using the genetic algorithms gave better results. Using this procedure several sets of solutions with zero misclassifications were obtained. Thus, the self-organizing maps may be considered as a promising tool for qualitative analysis of urinary calculi.