A Novel Evolutionary Algorithm for Mining Noisy Survey Datasets with an Application Toward Combating Chagas Disease

Chagas disease is a deadly, neglected tropical disease endemic to every country in Central and South America. The principal vector of Chagas disease in Central America is the insect Triatoma dimidiata. The best methods of preventing household infestation with T. dimidiata (including Ecohealth interventions) involve mining large amounts of socioeconomic and entomologic survey data (comprised of nominal and ordinal data types) for numerous potential risk factors. The number of risk factors suggested by experts is too large for exhaustive search; and the use of traditional statistics can exclude risk factors that are purely epistatic. Therefore, we apply a novel evolutionary algorithm, the conjunctive clause evolutionary algorithm (CCEA), to mine these “Big Datasets” for the most important risk factors associated with T. dimidiata infestation using georeferenced survey data from two villages in Guatemala as examples. The CCEA identified socioeconomic risk factors to be important that are not
significant using traditional statistics.