Intelligent Solution for Classification of Diseases Transmitted by Vector Aedes Aegypti

Abstract

Several physical or emotional factors can contribute negatively to critical moments in the health area, negatively influencing the diagnosis of diseases. Therefore, this work proposes an intelligent solution based on classifiers as an inference mechanism capable of assisting health professionals during the process of clinical management of diseases transmitted by the Aedes Aegypti mosquito, identifying the most probable diagnosis based on symptoms and outcome of exams. Thus, two learning models capable of inferring the probability of a patient being infected with a particular disease were applied, with an accuracy up to 91.6%. An intelligent API to support decision-making was then built during the clinical management of dengue and chikungunya. The solution allows several applications to access learning models. As proof of concept, a mobile application of popular consultation for the identification of dengue and chikungunya was also developed.