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

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

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