International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 7 Issue 4
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Early Prediction of Diabetes Using Logistic Regression

Author(s) Ms. Kanaga lakshmi S, mr harish M, Ms. Swathi S, Gopal samy B
Country India
Abstract Diabetes mellitus is a long-term health issue marked by the inadequate control of blood sugar levels, which can result in serious complications such as heart disease, kidney failure, nerve damage, and loss of vision. Successful diabetes management largely hinges on keeping blood glucose levels within a healthy range. Continuous glucose monitoring (CGM) systems have transformed diabetes management by providing instant feedback on glucose levels, enabling the quick identification of both hyperglycemia (high blood sugar) and hypoglycemia (low blood sugar) incidents.

Although advanced machine learning techniques, like neural networks, have been utilized to accurately forecast changes in glucose levels, these models typically demand substantial computational power and often lack clarity, making them less appropriate for everyday clinical applications. This research investigates the possibility of using a simpler and more transparent method, logistic regression, to predict short-term risks associated with glycemic levels. By utilizing CGM data alongside clinical information, the study seeks to create a cost-effective and efficient model that can be applied in real-time healthcare environments.
Keywords Diabetes management, Glycemic risk prediction, Logistic regression, Continuous glucose monitoring (CGM), Blood glucose forecasting, Data preprocessing, Predictive modeling, Short-term prediction
Field Engineering
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-07-25
DOI https://doi.org/10.36948/ijfmr.2025.v07i04.51646
Short DOI https://doi.org/g9vpgc
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