Modeling Broncho-pneumonia Status In Infants Using Discriminant And Logisic Regression Analyses

This work applies Discriminant Analysis and Logistic Regression models to predict the prevalence of Broncho-Pneumonia status (BPn) in infants. The data used in this study were collected from two tertiary health institutions in North Central Zone; University Teaching Hospital (UTH), Abuja and Federal Medical Centre (FMC), Keffi, Nassarawa State. Five predictors which are well-recognized for characterizing broncho-pneumonia in infants (baby‟s weight at birth, baby‟s weight 4week after, sex, mother‟s age and mother‟s occupation) were considered. One hundred and eighty (180) and two hundred and fifty three (253) infants with Low Birth Weight (LBW) were randomly sampled using simple random sampling technique from UTH, Abuja and FMC, Keffi respectively to build up the models. Both Linear Discriminant and Logistic Regression Models were fitted to the data for the two groups, and the best model was identified. Ten different samples of size 10 each were randomly taken from the dataset using SPSS package. The new datasets were used to validate the two models. It was observed that Discriminant Model is better used in the zone than Logistic Regression Model. We also find out that baby‟s weight at birth is best at discriminating between the two groups, since it has the least value of Wilk‟s Lambda compare to other predictor variables.

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