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All Journal Algoritma: Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian dan Angkasa
Lubna Jaafar Hussein Ibrahim
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Earth & Planetary Sciences Mathematics

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Using the Gamma Regression Model in Diagnosing Neonatal Jaundice among Newborns in Diyala Province Gheada Ibrahim Sheab; Lubna Jaafar Hussein Ibrahim; Nawal Muhammad Yaqoub Khalil; Sura Anwar Jameel
Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa Vol. 3 No. 6 (2025): Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/algoritma.v3i6.780

Abstract

This paper applies the Gamma Regression Model to determine the probability of newborns in the Diyala Province developing neonatal jaundice, or hyperbilirubinemia. This disease is prevalent, and it may be severe due to the large amount of unconjugated bilirubin in the blood. The aim of the study was to test the relationship between the levels of bilirubin and a set of independent variables, including the weight of birth, gestational age, and the proportion of red blood cells (PVC). 67 worth of data regarding neonatal cases was collected, and the outcome was that the model fitted well. The findings further indicated that whereas the influence of PVC was significant and positive on the bilirubin level, lower gestational age and less weight at birth had significant negative influence. As the results of the study indicate, the Gamma Regression Model is an effective tool to assess medical data and predict critical scenarios, which assists a clinician with the timely and accurate decision-making.
Co-Authors Gheada Ibrahim Sheab Nawal Muhammad Yaqoub Khalil Sura Anwar Jameel