Predicting Prediabetes Using Simple a Multi-Layer Perceptron Neural Network Model

In over 60% of patients with prediabetes, the evolution to diabetes can be stopped by changing lifestyle. Application of prediabetes criteria existing in accredited guidelines is very useful, representing an effective way to avoid prediabetes and diabetes. Although these guidelines imposed by the international diabetes federation are constantly updated, many doctors do not apply, mainly due to lack of time, the recommended steps for diagnosis and treatment. In this paper, a multi-layer perpeptron neural network model for prediabetes prediction is proposed, based on a dataset with 125 persons (men and women), with the following features: gender (S), serum glucose (G), serum triglycerides (TG), serum high-density lipoprotein cholesterol (HDL), waist circumference (WC) and systolic blood pressure (SBP). The output feature in the dataset (prediabetes or not) was based on a standardized medical criterion named Adult Treatment Panel III Guidelines (ATP III), which specifies that prediabetes diagnostic can be establish if at least three of five parameters are outside the scale of their normal values. Satisfactory results were obtained in evaluating the model.