  The use of machine learning algorithms in healthcare can amplify social
injustices and health inequities. While the exacerbation of biases can occur
and compound during the problem selection, data collection, and outcome
definition, this research pertains to some generalizability impediments that
occur during the development and the post-deployment of machine learning
classification algorithms. Using the Framingham coronary heart disease data as
a case study, we show how to effectively select a probability cutoff to convert
a regression model for a dichotomous variable into a classifier. We then
compare the sampling distribution of the predictive performance of eight
machine learning classification algorithms under four training/testing
scenarios to test their generalizability and their potential to perpetuate
biases. We show that both the Extreme Gradient Boosting, and Support Vector
Machine are flawed when trained on an unbalanced dataset. We introduced and
show that the double discriminant scoring of type I is the most generalizable
as it consistently outperforms the other classification algorithms regardless
of the training/testing scenario. Finally, we introduce a methodology to
extract an optimal variable hierarchy for a classification algorithm, and
illustrate it on the overall, male and female Framingham coronary heart disease
data.
