Paper Contents
Abstract
Diabetes mellitus poses a significant public health challenge globally, particularly in low-resource settings where diagnostic facilities are often limited and access to healthcare is constrained. This study investigates the use of predictive analytics as a cost-effective tool for the early detection of diabetes in such areas, aiming to bridge the gap in healthcare accessibility and enhance disease management. We developed and validated several predictive models by employing advanced statistical and machine learning techniques to analyze demographic and clinical data collected from healthcare centers in three under-resourced regions. These models were trained on variables including age, gender, body mass index (BMI), family history, and basic laboratory results. The validation process involved rigorous cross-validation techniques to ensure the robustness and reliability of the predictions.
Copyright
Copyright © 2025 Anshuma Jain. This is an open access article distributed under the Creative Commons Attribution License.
