Stem Girls and Covid-19
Serifat OlageshinFolorunso
January 28, 2021
ISCB Conference, Vigo Spain
Serifat OlageshinFolorunso
January 27, 2021
UseR Conference, Toulouse France
Serifat OlageshinFolorunso
January 27, 2021
Academic Visitation to Queensland University of Technology
Serifat OlageshinFolorunso
January 27, 2021
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.
Determinant of Neonatal Jaundice: A Logistic Regression and Correspondence Analysis Approach.
Serifat OlageshinFolorunso
January 27, 2021
Prevalence and factors associated with neonatal jaundice: a case study of University College Hospital, Ibadan
Serifat OlageshinFolorunso
January 27, 2021