Vishwa Vallabh Angampally and Eugene Pinsky, Metropolitan College Boston University, USA

We often rely on human experts to assign true labels in medical datasets, which may not be 100% accurate. We investigate the impact of labeling errors on machine-learning classi- fiers applied to medical datasets. By introducing symmetric errors from 0% to 40% in True labels— simulating errors of true labels assignment by experts, inter-observer variability, and automated annotation - we evaluate the impact of such errors in binary classification for several well-known medical datasets using traditional machine learning models and metrics. Although all models experience degradation as errors increase, simpler, well-regularized methods such as Logistic Regression and SVM decline more gracefully. Our results underscore the necessity for improved data curation and error-aware training strategies in medical AI, ultimately guiding the selection of robust algorithms that maintain reliability under imperfect real-world conditions.

Labeling Errors, Medical Datasets, Classifier Robustness, Data Imbalance, Machine-Learning, Performance Evaluation, Diagnostic Prediction.