Determinant of Neonatal Jaundice: A Logistic Regression and Correspondence Analysis Approach.
In this work, we use binary logistic regression which measures the response outcome that has two categories, and correspondence analysis that is conceptually similar to principal component analysis but applies to categorical outcomes. This study examines determinants of neonatal jaundice and proposes a qualitative response regression model for obtaining precise estimates of the probabilities of neonates having neonatal jaundice. Logistic regression analysis and correspondence analysis are used to model neonatal jaundice as a response variable while the covariates are neonate's age, sex, birth-weight, mode of delivery, place of delivery, settlement, G6PD, Rhesus-factor, mother-illness, mother-education, parity, and gestational age. The model converges at the 4th iteration with a log-likelihood of-133.94965 and the McFaddenpseudo-R2 is 0.1663 with the probability of 0.0000 at 5% α level of significance, this indicated that the model fitted for the study is adequate at that level of significance. In conclusion, the performance of the model is reliable, useful, and proves the existence of risk factors that determine neonatal jaundice.
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