USING ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LOGISTIC REGRESSION ANALYSIS TO STUDY SOME FACTORS RESULTING LEUKEMIA

Abstract

This study investigated some factors which lead to leukemia using, artificial neural networks (ANN) and multiple logistic regression analysis. The study also compared between these methods determine which method is better. The study included 150 observations and four variables: Chronic myeloid leukemia (CML) as a response variable, patient weight, blood platelets (PLT), and the bone marrow edema (EME) as explanatory variables. The model's goodness of fit was measured using the maximum likelihood ratio test, which followed a chi-square distribution. Results showed that the relationship between the explanatory variables and response variables is significant, indicating the importance of the explanatory variables in the model. The logistic regression model (LRM)'s parameters were estimated using the maximum likelihood ratio, and the neural network method was also used to analyze the data. The neural network method achieved a much higher accuracy rate than the multiple logistic regression method, with a difference of 20%, indicating the superiority of the neural network method over the multiple logistic regression method.