Prediction of diabetic patients in Iraq using binary dragonfly algorithm with long-short term memory neural network

Abstract

Over the past 20 years, there has been a surge of diabetes cases in Iraq. Blood tests administered in the absence of professional medical judgment have allowed for the early detection of diabetes, which will fasten disease detection and lower medical costs. This work focuses on the use of a Long-Short Term Memory (LSTM) neural network for diabetes classification in Iraq. Some medical tests and body features were used as classification features. The most relevant features were selected using the Binary Dragon Fly Algorithm (BDA) Binary version of the selection method because the features either selected or not. To reduce the number of features that are used in prediction, features without effects will be eliminated. This effects the classification accuracy, which is very important in both the computation time of the method and the cost of medical test that the individual will take during annual check ups.This work found out that among 11 features, only five features are most relevant to the disease. These features provide a classification accuracy up to 98% among three classes: diabetic, non diabetic and pre-diabetic.