ON COMPARING MULTI-LAYER PERCEPTRON AND LOGISTIC REGRESSION FOR CLASSIFICATION OF DIABETIC PATIENTS IN FEDERAL MEDICAL CENTER YOLA, ADAMAWA STATE
Keywords:Logistic Regression, Multi-Layer Perceptron, Artificial Neural Network, Log-Likelihood Ratio, Diabetes Mellitus
The logistic regression (LR) and Multi-Layer (MLP) are used to handle regression analysis when the dependent response variable is categorical. Therefore, this study assesses the performance of LR and MLP in terms of classification of object/observations into identified component/groups. A data set consists of 553 cases of diabetes were collected at Federal Medical Center, . 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 ), 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 Logistic regression analysis and the multi-Layer , 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 LP classifies diabetic patients correctly with 91.8% accuracy. it classifies non-diabetic patients with 89.1% accuracy. 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 while LR classifies with 90.6% accuracy. This study complements other where MLP, a type Artificial neural network classifies and predicts better than other non-neural network classifiers.
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