Machine Learning Model for Contemporary Dry Eye Disease Risk Factors: A Study on the Integrated Approach of Logistic Regression Model and Statistical Verification

Dry eye disease is rapidly spreading in modern society due to increased use of digital devices and environmental factors, and it is an important public health issue that can lead to corneal damage and permanent vision loss if not properly managed. As various factors such as lifestyle, environment, and heredity act in combination, establishing an early screening model using measurable indicators in daily life is key to establishing a prevention strategy. In this study, the machine learning model and binary logistic regression analysis using SPSS(Statistical Package for the Social Sciences) were performed in parallel to secure predictive power and statistical validity at the same time. As a result of the analysis, the reliability of the model was proved by matching the rankings of the top six risk factors derived from machine learning and SPSS analysis. The results combine the efficiency of flexible machine learning with statistical interpretation power in data structure change, and are expected to be an important basis for the development of customized digital healthcare and early screening tools centered on symptoms in the future.