Evolving A Neural Network to Predict Diabetic Neuropathy

Abstract

One of the main areas where machine learning (ML) techniques are used vastly is in prediction of diseases. Diabetic neuropathy (DN) disease is a complication of diabetes which causes damage to nerves. Early prediction of DN helps diabetic patient to avoid its complications. The main aim of this work is to identify various risk factors of DN and predict it accurately using ML techniques. Radial basis function (RBF) network is an artificial neural network proposed to obtain better results than traditional ML classification techniques. CART, random forest and logistic regression are existing classification techniques considered. Accuracy, recall, f1 score, area under ROC curve (AUC), Matthews correlation coefficient (MCC) and kolmogorov-smirnov (KS) statistic are performance metrics used to evaluate and compare algorithms. From comparative study it was observed that proposed technique RBF network performed better. The performance metric values obtained for RBF network are accuracy-68.18%, recall-0.909, f1score-0 .7407, AUC-0.6405, MCC-0.4082 and KS statistic-0.5417. Accordingly, the use of RBF network while predicting DN gives accurate and better results.

Keywords

• diabetic neuropathy (dn)
• diabetes
• machine learning (ml)
• artificial neural network (ann)
• radial basis function (rbf) network
• cart
• random forest (rf)
• logistic regression (lr)
• accuracy
• recall
• f1 score
• area under roc curve (auc)
• matthews correlation coefficient (mcc) and kolmogorov-smirnov statistic (ks)