Development ofPredictive Model for Levels of Glycemic Control as a Measure of Diabetes Self-management

Abstract

Diabetes mellitus is a chronic metabolic disorder that affects millions of people worldwide and has been related to serious complications such as cardiovascular disease, neuropathy, nephropathy, and retinopathy. Effective diabetes care is dependent on maintaining glycemic control, which is frequently measured using glycated hemoglobin (HbA1c) values. HbA1c is an important biomarker that represents average blood glucose levels over the previous two to three months and is used to determine long-term glycemic management and the risk of problems. The goal of this research is to create a reliable and accurate HbA1c prediction model utilizing machine learning (ML) technologies. The objectives include assessing clinical and demographic data to find significant variables, creating and comparing multiple machine learning models, and determining the best effective model for HbA1c prediction. In addition, the study investigates the clinical consequences of precise HbA1c forecasts, as well as the obstacles and ethical problems involved with machine learning in health care. The study indicated that Gradient Boosting Regression (GBR) outperformed other models, with the lowest Mean Squared Error (MSE) and greatest R² values. Random Forest Regression also fared well, although Neural Network Regression (NNR) and Support Vector Regression (SVR) were less effective due to their sensitivity to feature scaling and parameter adjustment. Accurate HbA1c forecasts can assist healthcare practitioners anticipate levels and enhance glycemic control, but issues like individual variability and data security must be addressed.