Artificial Neural Networks Model for Predicting Type 2 Diabetes Mellitus Based on Gene Polymorphism, Lipid Profile and Demographic Data.

Type 2 diabetes mellitus (T2DM) is a multifactorial disease associated with many genetic polymorphisms; among them is the polymorphism in the vitamin D receptor () gene. In this case-control study, samples from 82 T2DM patients and 82 healthy controls were examined to investigate the association of the polymorphism and lipid profile with T2DM in the Jordanian population. DNA was extracted from blood and genotyped for the polymorphism by polymerase chain reaction (PCR) and DNA sequencing. Lipid profile and fasting blood sugar were also measured. There were significant differences in high-density lipoprotein (HDL) cholesterol and triglyceride levels between T2DM and control samples. Frequencies of the polymorphism (CC, CT and TT) were determined in T2DM and control samples and were not significantly different. Furthermore, there was no significant association between the polymorphism and T2DM or lipid profile. A feed-forward neural network (FNN) was used as a computational platform to predict the persons with diabetes based on the polymorphism, lipid profile, gender and age. The accuracy of prediction reached 88% when all parameters were included, 81% when the polymorphism was excluded, and 72% when lipids were only included. This is the first study investigating the association of the gene polymorphism with T2DM in the Jordanian population, and it showed negative association. Diabetes was predicted with high accuracy based on medical data using an FNN. This highlights the great value of incorporating neural network tools into large medical databases and the ability to predict patient susceptibility to diabetes.