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.


Download data is not yet available.


Agresti Alan. (2002). Categorical Data Analysis. 2nd Edition. University of Florida. New York: Wiley
Chao, S. and Wong, F. (2009). An incremental decision tree learning methodology regarding attributes in medical data mining. Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding.
Hosmer, D. W. &Lemeshow, S., & Klar, J. (1988). A goodness-of- fit test for the multiple regression model. Communications in Statistics, A10, 1043-1069.
Hosmer, D. W &Lemeshow, S. (2000). Applied Logistic Regression, 2nd ed, 1-2. Wiley and sons inc.
Joaquim, P., Marques de Sá. (2007). Applied Statistics Using SPSS, STATISTICA, MATLAB and R. 2nd Edition. Springer-Verlag Berlin.
Karegowda, A.G. and Jayaram, M.A. (2009). Cascading GA & CFS for feature subset selection in medical data mining. IEEE: 1-4.
Lewicki, P, Hill. T. (2006). Statistics: Methods and Applications: Comprehensive Reference for Science, Industry, and Data Mining. StatSoft, Inc.
Muhammad, M. U., Jiadong, R., Sohail, M. N., Irshad, M., Bilal M., Osi, A. A., (2018). A logistic regression modeling on the prevalence of diabetes mellitus in the North Western Part of Nigeria, Benin Journal of StatisticsVol. 1, pp. 1- 10.
Podgorelec, V. and Maribor, H.M. (2005). Improving mining of medical data by outliers pre-dictions. IEEE: 1-6.
Stephen. V. (1997) Selecting and Interpreting measures of thematic classification accuracy. Remote sensing of environment, 77-89.
Tang, P.H. and Tseng, M.H. (2009). Medical data mining using BGA & RGA for weighting of features in Fuzzy KNN classi_cation. IEEE, 5: 1-6.
Wang, S. and Zhou, G.G. (2005). Application of fuzzy clusters analysis for medical imagedata mining. IEEE, 2: 1-6.
Wehrens, Ron. (2010). Chemometrics with R Multivariate Data Analysis in the Natural Sciences and Life Sciences. Springer.
Xue, W. and Yanan S. Y. (2006). Research and application of data mining in traditional Chinese medical clinic diagnosis. IEEE, 4: 1-4.



How to Cite

Ahmed, H., Mohammed, M. B., & Baba, I. A. (2021). ON COMPARING MULTI-LAYER PERCEPTRON AND LOGISTIC REGRESSION FOR CLASSIFICATION OF DIABETIC PATIENTS IN FEDERAL MEDICAL CENTER YOLA, ADAMAWA STATE. International Journal of Engineering Technologies and Management Research, 8(6), 24–40. https://doi.org/10.29121/ijetmr.v8.i6.2021.961