Multi-Layer Perceptron for Classification of Diabetic Patients: A Case of Federal Medical Centre, Yola

Abstract

Multi-Layer Perceptron (MLP) are used to handle regression analysis when the dependent response variable is categorical. Therefore, this study assesses the performance of MLP in terms of classification of object/observations into identified component/groups. A data set consists of 553 cases of diabetes mellitus were collected at Federal Medical Center, Yola. The variables measured: Age(years), Mass of a patient (kg/meters), glucose level (plasma glucose concentration, a 2-hour in an oral glucose tolerance test), pressure (Diastolic blood pressure mmHg), insulin (2-hour serum insulin mu U/ml) and class variable (0 or 1) treating 0 as false or negative and 1 treated as true or positive test for diabetes. The method used in the study is the Multi-Layer perceptron, a type of Artificial Neural Network, confusion matrix, classification, network algorithm and SPSS version 21 for Windows 10.1. The result of the study showed that MLP classifies diabetic patients with 88.6% accuracy while it classifies Non-diabetic patients with 93.2% classification accuracy. Overall, MLP classifies better with 91% accuracy. This study complements other literatures where MLP, a type Artificial neural network classifies and predicts better than other Non-neural network classifiers. Remarkably, the results indicate the effectiveness of applying MLP as significant method to predict and classify diabetic and non-diabetic patients’ to help in understanding variables that may influence diabetic treatment.