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The existence of nosocomial infection prediction systems in healthcare environments can contribute to improve the quality of the healthcare institution. Also, can reduce the costs with the treatment of those patients. The analysis of the information available allows to efficiently prevent these infections and to build knowledge that can help to identify the eventual occurrence of nosocomial infections. Good models induced by the DM classification techniques SVM, DT and NB, were achieved (sensitivities higher than 91.90%). Therefore, this system is able to predict these infections consequently, reduce the nosocomial infection incidence. The platform developed presents important information, as well as supports healthcare professionals in their decisions, namely in planning infection prevention measures. So, the system acts as a CDSS capable of reducing nosocomial infections and the associated costs, improving the …