Aplicação de algoritmos de mineração de dados para classificação molecular de Leptospira spp

Abstract

Leptospirosis is an infectious disease of global importance that affects humans and animals caused by pathogenic spirochetes belonging to the genus Leptospira. For epidemiological and clinical areas, it is essential that laboratories have the ability to identify and classify accurately species of Leptospira that cause disease, to take decisions consistent with respect to public health. In this study, we report for the first time the use of data mining tools for the purposes of strain classification of the genus Leptospira. Twenty-five loci related to 15 genes were selected and analyzed in 600 Leptospira draft genomes in order to search polymorphisms that could be used for the classification of each species. For this, data mining-based algorithms - C4.5, Naive Bayes and SVM - were used. All data mining computational algorithms used in this study showed accuracy levels above 93% for Leptospira classification species, however, the C4.5 algorithm achieve the best accuracy rating (95.6%) and presented the genes that contributed to the final result of the analysis. The same genomic database used by computer algorithms has been tested with the MLST methodology – most used technique for molecular classification of species of this genus - however, none of the tests show accuracy higher to 80%. Because data mining algorithm C4.5 achieve better accuracy than other algorithms, it can be concluded that C4.5 is a very promising data mining tool to classify species of Leptospira.