MULTINOMIAL LOGISTIC REGRESSION OF GENETIC DISORDERS IN CHILDREN

Abstract

Genetic disorders in children present significant public health concerns due to their lifelong impact on physical, cognitive, and emotional development. Understanding the hereditary patterns and associated risk factors is crucial for early diagnosis, prevention, and informed reproductive decisions. This study explored the practical application of the odds ratio in examining the relationship between inherited genes and the likelihood of developing specific types of genetic disorders in children. The research utilized a dataset obtained from Kaggle.com, comprising six critical variables: paternal gene, maternal gene, gene in mother’s side, history of previous pregnancies, and status (alive or deceased). A multinomial logistic regression model was employed for the analysis, using IBM SPSS Statistics version 20. The model demonstrated a good fit with the data and revealed insightful associations between inherited genes and the manifestation of particular genetic disorders. Findings indicated that children inheriting genes from their mother (odds ratio: 1.88) or father (odds ratio: 1.87) had a higher likelihood of developing mitochondrion disorders compared to single gene disorders. Similarly, the presence of maternal genes (odds ratio: 1.60) and paternal genes (odds ratio: 1.49) was associated with an increased risk. These results underscore the importance of genetic screening and counseling. It is recommended that parents undergo genetic testing to better understand the genetic traits they may pass to their offspring, potentially reducing the risk of severe hereditary conditions.