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Broncho Pulmonary Dysplasia (BPD) is a form of chronic lung disease that develops in preterm
neonates treated with oxygen and positive-pressure ventilation. The disease affects premature
babies and contributes to their morbidity and mortality. This research seeks to fit and compare
the predictive powers of Logistic Regression (Logit) Modeland Probability Regression (Probit)
Model in tracking infants’ BPD status using gender and weights at two different time intervals.
The data used for the analysis were samples of 50 infants drawn from an underlying population
of children with low birth weight (g) from Ahmadu Bello University Teaching Hospital Zaria.
The children were confined to a neonatal intensive care unit, where they require intubation
during the first 12 hours of life, and they survive for at least 28 days and their weights measured
four weeks later. The results obtained found explanatory variables (weight at birth, weight after
Four weeks of life and gender) used to be significantly associated with the occurrence of BPD in
infants and suggested that, Probit fits BPD data more than Logit. It is therefore recommended
that Clinics should adopt the use of the probit model fitted by this research to detect prevalence
of BPD among infants so that adequate measures for prevention and control can be put in place
early enough to signal the danger of the full manifestation of the disease.
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SIMILAR STATISTICS FINAL YEAR PROJECT RESEARCH TOPICS
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