Application of a Decision Tree Model for Predicting Diabetic Retinopathy

Diabetic retinopathy (DR) is one of the common complications among diabetes patients, characterized by a high incidence rate and difficult treatment, significantly affecting the quality of patients' lives and productivity. This condition significantly undermines the quality of life and productivity of the individuals it afflicts. Therefore, the capability to accurately predict the emergence and development of diabetic retinopathy (DR) can equip healthcare providers with the means to intervene and provide timely treatments, thereby substantially reducing the likelihood of additional complications. This paper employs the classic C4.5 algorithm from decision tree algorithms to construct a model for predicting diabetic retinopathy (DR). First, we introduce the foundational concepts of decision tree algorithms along with common methods for assessing importance. Following this, the C4.5 algorithm is utilized to develop and assess a decision tree model. This process involves refining the model through careful parameter tuning and the elimination of irrelevant variables. A comparative analysis of model accuracies is conducted, revealing that this approach significantly enhances the model's predictive accuracy and overall performance, as evidenced by the analysis of experimental data.