Exploring Machine Learning Models for Predicting Diabetic Retinopathy: A Comprehensive Comparative Study of Logistic Regression an Advanced Technique

Abstract

This research provides a comprehensive examination of machine learning models for predicting diabetes-related ocular diseases, with a focus on Logistic Regression versus more advanced approaches. A large dataset encompassing a variety of diabetes-related lifestyle and health factors is used in the study to extensively train and analyze multiple models in order to demonstrate their predictive utility. The thorough evaluation results illuminated the subtle differences in performance between Logistic Regression and other advanced algorithms, offering insightful information about the pros and cons of each in terms of predicting the risk of diabetic retinopathy and other complications relating to the eyes. The findings reveal crucial themes for additional research and advancement in the realm of predictive modeling for diabetic eye disorders, in the process of verifying that logistic regression works well in specific situations.