An Optimized Recursive General Regression Neural Network Oracle for the Prediction and Diagnosis of Diabetes

Abstract

Diabetes is a serious chronic disease that has been seeing a rise in the number of cases and prevalence over the past few decades It can lead to serious complications and can increase the overall risk of dying prematurely Data-oriented prediction models have become effective tools that help medical decision-making and diagnoses in which the use of machine learning in medicine has increased substantially This research introduces the Recursive General Regression Neural Network Oracle R-GRNN Oracle and is applied on the Pima Indians Diabetes dataset for the prediction and diagnosis of diabetes The R-GRNN Oracle Bani-Hani 2017 is an enhancement to the GRNN Oracle developed by Masters et al in 1998 in which the recursive model is created of two oracles one within the other Several classifiers along with the R-GRNN Oracle and the GRNN Oracle are applied to the dataset they are Support Vector Machine SVM Multilayer Perceptron MLP Probabilistic Neural Network PNN Gaussian Na ve Bayes GNB K-Nearest Neighbor KNN and Random Forest RF Genetic Algorithm GA was used for feature selection as well as the hyperparameter optimization of SVM and MLP and Grid Search GS was used to optimize the hyperparameters of KNN and RF The performance metrics accuracy AUC sensitivity and specificity were recorded for each classifier The R-GRNN Oracle was able to achieve the highest accuracy AUC and sensitivity 81 14 86 03 and 63 80 respectively while the optimized MLP had the highest specificity 89 71