Implementation of Fuzzy Cognitive Maps with Genetic Algorithms for Predicting Type 2 Diabetes Mellitus
Abstract
Type 2 diabetes mellitus is a chronic non-communicable disease caused by a disorder in glucose metabolism, which results in an abnormal increase in glucose concentration in the blood. The late diagnosis of this disease contributes to the increased worldwide rates of morbidity and mortality. The development of models based on artificial intelligence for the prediction of diabetes could accelerate diagnosis. Therefore, the aim of the present study was to develop a prediction model for type 2 diabetes mellitus based on fuzzy cognitive maps trained with a genetic algorithm. The methodology employed consisted of using a dataset from the National Institute of Diabetes and Digestive and Kidney Diseases PIMA Indian population, which contains demographic and clinical information from 768 patients. For training and validation, 70 % of the data was used and the remaining 30 % was used for performance testing. The fuzzy cognitive map model can predict the disease with 99 % accuracy, 98 % precision, and 100 % recall. It is concluded that the model presents a good ability to predict and evaluate the behavior of the variables of interest in type 2 diabetes mellitus, showing its value as a support tool for the timely identification of the disease and support for decision making by the physician.
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Funding data
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Universidad Cooperativa de Colombia
Grant numbers INV3569
