INTELLIGENT DATA SYSTEMS FOR SUSTAINABLE PREVENTION AND MANAGEMENT OF G6PD DEFICIENCY: A MARKOV CHAIN MODEL APPROACH

Abstract

Adopting sustainable healthcare practices is crucial for effectively managing genetic disorders. Despite significant advances in our understanding of the genome over the last decade, there are notable limitations in epidemiological and analytic approaches for investigating the effects of common chronic genetic diseases like Glucose-6-phosphate dehydrogenase (G6PD) deficiency in Oman. This study aims to use pedigree charting and Markov Chain Model analysis to determine the pattern of gene distribution for G6PD deficiency in the family trees of Omani nationals. It also identifies the pattern of gene distribution for G6PD deficiency by synchronizing medical data, including the genetic history of families, and applying Markov's model to find the family generation at which G6PD deficiency becomes normal homozygote and reaches an equilibrium state. Based on the analysis using the Markov Chain Model, it is observed that after a few generations, the population attains a normal homozygote state. However, this equilibrium is not consistent across all families. In some families, the generation becomes normal after 18 stages, while in others it becomes normal after 12 stages. Leveraging intelligent data systems can reduce long-term healthcare costs and improve population health, ultimately fostering economic sustainability in Oman.