Abstract :

Diabetes Is A Chronic Metabolic Disorder Characterized By Persistently Elevated Blood Glucose Levels. Over Time, This Condition Often Leads To Several Comorbidities, Including Neuropathy, Cardiovascular Disease, Renal Complications, And Kidney Failure. Early Detection And Prediction Of These Comorbidities Are Crucial For Timely Intervention And Improved Patient Outcomes. Traditionally, Clinical Observations And Statistical Analyses Formed The Foundation For Managing Diabetic Comorbidities. While Valuable, These Approaches Were Limited In Their Ability To Capture The Complexity And Diversity Of Comorbidity Patterns. The Advent Of Machine Learning (ML) Has Revolutionized This Field By Enabling The Development Of Advanced, Data-driven Predictive Models With Greater Precision And Reliability. However, Building Effective ML Models For Detecting And Predicting Diabetes-related Comorbidities Requires Comprehensive Patient Datasets That May Include Demographics, Medical History, Diagnostic Test Results, And Even Genetic Information. Unlike Manual Methods, Which Are Often Constrained By Human Subjectivity And The Inability To Process Large Volumes Of Data, ML Algorithms Can Efficiently Analyze Massive Datasets, Uncovering Hidden Correlations And Complex Patterns That Might Otherwise Remain Undetected. Accurate And Timely Prediction Of Comorbidities Is Essential, As These Conditions Significantly Reduce The Quality Of Life In Diabetic Patients. Machine Learning Offers The Potential To Support Personalized Healthcare By Continuously Learning From Historical Data, Integrating New Information, And Refining Predictive Accuracy Over Time. By Identifying Key Risk Factors And Forecasting Comorbidity Development, ML-driven Approaches Pave The Way For Earlier Interventions And More Effective Treatment Strategies. In This Context, Machine Learning Represents A Transformative Step Toward Personalized Medicine, Offering Significant Promise In Reducing The Burden Of Comorbidities And Impr

Published:

26-8-2025

Issue:

Vol. 25 No. 8 (2025)

Page Nos:

459-475

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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