  Hypertrophic cardiomyopathy (HCM) patients have a high incidence of atrial
fibrillation (AF) and increased stroke risk, even with low risk of congestive
heart failure, hypertension, age, diabetes, previous stroke/transient ischemic
attack scores. Hence, there is a need to understand the pathophysiology of AF
and stroke in HCM. In this retrospective study, we develop and apply a
data-driven, machine learning based method to identify AF cases, and clinical
and imaging features associated with AF, using electronic health record data.
HCM patients with documented paroxysmal/persistent/permanent AF (n = 191) were
considered AF cases, and the remaining patients in sinus rhythm (n = 640) were
tagged as No-AF. We evaluated 93 clinical variables and the most informative
variables useful for distinguishing AF from No-AF cases were selected based on
the 2-sample t test and the information gain criterion. We identified 18 highly
informative variables that are positively (n = 11) and negatively (n = 7)
correlated with AF in HCM. Next, patient records were represented via these 18
variables. Data imbalance resulting from the relatively low number of AF cases
was addressed via a combination of oversampling and under-sampling strategies.
We trained and tested multiple classifiers under this sampling approach,
showing effective classification. Specifically, an ensemble of logistic
regression and naive Bayes classifiers, trained based on the 18 variables and
corrected for data imbalance, proved most effective for separating AF from
No-AF cases (sensitivity = 0.74, specificity = 0.70, C-index = 0.80). Our model
is the first machine learning based method for identification of AF cases in
HCM. This model demonstrates good performance, addresses data imbalance, and
suggests that AF is associated with a more severe cardiac HCM phenotype.
