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

Abstract

Type 2 diabetes mellitus (T2DM) is a multifactorial disease associated with many genetic polymorphisms; among them is the FokI polymorphism in the vitamin D receptor (VDR) gene. In this case-control study, samples from 82 T2DM patients and 82 healthy controls were examined to investigate the association of the FokI polymorphism and lipid profile with T2DM in the Jordanian population. DNA was extracted from blood and genotyped for the FokI 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 FokI 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 FokI 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 FokI polymorphism, lipid profile, gender and age. The accuracy of prediction reached 88% when all parameters were included, 81% when the FokI polymorphism was excluded, and 72% when lipids were only included. This is the first study investigating the association of the VDR gene FokI 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.

Data Mining Classification Techniques for Diabetes Prediction

“…judgment limits to define data points are hyperplane. The hyperplane classifies the data points with the highest classhyperplane margin [42]. SVM is a controlled model of computer education.…”

“…Also, "TABLE 12" outlines the relation of this analysis to the studies analyzed by the researchers in the related work. [16] 1 41 and dataset 2 42 -NCD -(SVC)…”

Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis

“…T2DM is mainly manifested by low insulin production by pancreatic cells and/or the produced insulin does not function effectively [ 4 ]. Many genetic factors and polymorphisms have been investigated in patients with T2DM; we have previously investigated that the vitamin D receptor ( VDR ) gene FokI polymorphism, the DNA-binding domain of regulatory factor X6 ( RFX6 ) gene, as well as the epoxide hydrolase ( EPHX2 ) gene rs4149243, rs2234914 and rs751142 variants [ 5 – 7 ].…”

“…On the other hand, recent years have witnessed an unprecedented development in the use of machine learning (ML) in various biotechnology, biomedicine, medical imaging and healthcare applications [ 27 – 30 ]. Supervised ML tools can be utilized to build predictive models involve the implementation of statistical means for learning and predicting disease status, either by including or excluding the polymorphisms genotypes [ 5 , 31 – 33 ]. The following are popular ML algorithms that were evaluated in the current research to predict T2DM and dyslipidemia based on the clinical parameters, demographic and polymorphism data: random forest (RF) [ 34 – 36 ]; naïve Bayesian (NB) [ 37 – 40 ]; eXtreme Gradient Boosting (XGBoost) [ 41 – 43 ]; k-nearest neighbors (kNN) [ 44 – 46 ], support vector machine (SVM) [ 47 , 48 ]; probabilistic neural networks (PNN) [ 49 – 53 ]; multilayer perceptron (MLP) [ 54 , 55 ]; adaptive boosting (AdaBoost) [ 56 , 57 ]; gradient boost [ 58 , 59 ]; and K-star (K*) [ 60 , 61 ].…”

Prediction and prognosis of acute myocardial infarction in patients with previous coronary artery bypass grafting using neural network model

“…Neural networks can find hidden features in input patterns that are not visible by conventional statistical methods. There are some studies that have shown that using of connectionist models for prediction of outcome in patients with coronary heart disease [14,15], the onset of diabetes [16,17] and other medical problems [18,19].…”