Avoiding Data Leakage in Machine Learning Models for Diabetes and Hypertension Classification Using Routine Occupational Health Screening Data
This study demonstrates that including outcome-defining predictors like fasting blood glucose and blood pressure in machine learning models for diabetes and hypertension classification causes severe data leakage and artificially inflated performance, whereas models excluding these variables using routine occupational screening data achieve moderate but clinically realistic accuracy while highlighting the predictive value of factors such as age, BMI, and psychological distress.