MULTINOMIAL LOGISTIC REGRESSION MODELLING OF IMPACT OF SELECTED DEMOGRAPHIC FACTORS ON PATIENTS’ BLOOD SUGAR LEVEL

Blood sugar irregularities is a chronic disease that affects millions of people worldwide. The measurement of blood glucose is typically expressed in millimoles per deciliter (mmol/dl), It is characterized by imbalance level of glucose (sugar) in the blood, resulting from the body’s inability to produce enough insulin or to use it effectively. There is currently no cure for imbalance blood sugar, but the condition can be effectively managed through various treatment approaches when detected at early stage. These treatments are aimed at helping the body regulate blood sugar levels, as maintaining good glucose control, it is essential for reducing the risk of blood sugar level related complications. This project therefore focuses on findings that may support the early detection of diabetes and encourage precautionary measures to prevent its onset. By using Multinomial Logistic Regression (MLR) to analyze demographic factors on 464 patients from a hospital in Abaji, Abuja. Demographic factors such as age, gender, body mass index (BMI), blood group, and genotype that are risk factors of blood sugar irregularities, age and BMI are widely known factors, past studies shows that over the age of 40 is at high risk as well as BMI ≥ 25 kg/m2. MLR is a statistical technique that provides valuable insights into the factors that influence the development of blood sugar irregularities, factors considered in this study showed significant effect except age and blood group. Ultimately, this work contributes to a better understanding of the condition and offers a foundation for improved prevention strategies.