Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status

Abstract

Abstract:Various methods can be applied to build predictive models for the clinical data with binary outcome variables.This research aims to compare and explore the process of constructing common predictive models. Models based on an artificial neural network (the multilayer perceptron) and binary logistic regression were applied and compared in their ability to classifying disease-free subjects and those with diabetes mellitus(DM) diagnosed by glucose level. Demographic, enthropometric and clinical data were collected based on a total of 460 participants aged over 30 years from six villages in Bangladesh that were identified as mainly dependent on wells contaminated with arsenic. Out of 460 participants 133 (28.91%) suffered from DM, 116 (25.27%) had impaired glucose tolerance (IGT) and the remainder 211 (45.86%) were disease free. Among other factors, family history of diabetes and arsenic exposure were found as significant risk factors for developing diabetes mellitus (DM), with a higher value of odds ratio. This study shows that, binary logistic regression correctly classified 73.79% of cases with IGT or DM in the training datasets, 70.96% in testing datasets and 70.4% of all subjects. On the other hand, the sensitivities of artificial neural network architecture for training and testing datasets and for all subjects were 83.4%, 82.25% and 84.33% respectively, indicate better performance than binary logistic regression model.

Machine Learning Data Imputation and Classification in a Multicohort Hypertension Clinical Study

“…13 Another study found that ANN performed better than binary logistic regression models in the detection of diabetes status from clinical data. 14 A study related to the prediction of heart disease using ANNs with a clinical data set of 13 variables is similar to this study, and they found higher accuracy, sensitivity, and specificity than a state vector machine comparison. 15 These and other studies show the usefulness of ANNs for pattern detection in real-world clinical data sets.…”

Modelling the prevalence of diabetes mellitus risk factors based on artificial neural network and multiple regression

“…It should be noted that due to the simplicity of interpretation of the variables in the logistic regression model, applying it clinically is more comprehensible. Rahman et al compared the accuracy of ANN and binary logistic regression models for predicting glucose status (21). They showed a significantly better performance of ANN for detection of impaired glucose tolerance and T2DM patients from disease-free ones (21).…”

“…Rahman et al compared the accuracy of ANN and binary logistic regression models for predicting glucose status (21). They showed a significantly better performance of ANN for detection of impaired glucose tolerance and T2DM patients from disease-free ones (21). Omurlu et al compared performance of logistic regression and ANN for prediction of albuminuria in T2DM and demonstrated that multilayer perceptron had the highest predictive capability for the presence of albuminuria (22).…”

Improved Functional Causal Likelihood-Based Causal Discovery Method for Diabetes Risk Factors

“…In recent years, rapid developments in artificial intelligence techniques have led to the adoption of machine learning methods to construct diagnostic and predictive models of various diseases. Intelligent diagnosis and prediction methods for different diseases can be classified into two categories: one based on traditional single learner and the other based on multiple learners, such as the diabetes diagnosis method based on a single learner proposed by Rahman et al [ 22 ] and the congestive heart failure diagnosis method based on multiple learners proposed by Isler et al [ 23 ]. In the diagnosis and prediction of diabetes mellitus, the approach based on a single learner can provide satisfactory results with higher efficiency.…”