Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls

Abstract

Purpose: Despite the potential of machine learning models, the lack of generalizability has hindered their widespread adoption in clinical practice. We investigate three methodological pitfalls: (1) violation of independence assumption, (2) model evaluation with an inappropriate performance indicator or baseline for comparison, and (3) batch effect. Materials and Methods: Using several retrospective datasets, we implement machine learning models with and without the pitfalls to quantitatively illustrate these pitfalls' effect on model generalizability. Results: Violation of independence assumption, more specifically, applying oversampling, feature selection, and data augmentation before splitting data into train, validation, and test sets, respectively, led to misleading and superficial gains in F1 scores of 71.2% in predicting local recurrence and 5.0% in predicting 3-year overall survival in head and neck cancer as well as 46.0% in distinguishing histopathological patterns in lung cancer. Further, randomly distributing data points for a subject across training, validation, and test sets led to a 21.8% superficial increase in F1 score. Also, we showed the importance of the choice of performance measures and baseline for comparison. In the presence of batch effect, a model built for pneumonia detection led to F1 score of 98.7%. However, when the same model was applied to a new dataset of normal patients, it only correctly classified 3.86% of the samples. Conclusions: These methodological pitfalls cannot be captured using internal model evaluation, and the inaccurate predictions made by such models may lead to wrong conclusions and interpretations. Therefore, understanding and avoiding these pitfalls is necessary for developing generalizable models.