  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.
