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Hypertrophic cardiomyopathy (HCM) patients have a high incidence of atrialfibrillation (AF) and increased stroke risk, even with low risk of congestiveheart failure, hypertension, age, diabetes, previous stroke/transient ischemicattack scores. Hence, there is a need to understand the pathophysiology of AFand stroke in HCM. In this retrospective study, we develop and apply adata-driven, machine learning based method to identify AF cases, and clinicaland imaging features associated with AF, using electronic health record data.HCM patients with documented paroxysmal/persistent/permanent AF (n = 191) wereconsidered AF cases, and the remaining patients in sinus rhythm (n = 640) weretagged as No-AF. We evaluated 93 clinical variables and the most informativevariables useful for distinguishing AF from No-AF cases were selected based onthe 2-sample t test and the information gain criterion. We identified 18 highlyinformative variables that are positively (n = 11) and negatively (n = 7)correlated with AF in HCM. Next, patient records were represented via these 18variables. Data imbalance resulting from the relatively low number of AF caseswas 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 logisticregression and naive Bayes classifiers, trained based on the 18 variables andcorrected for data imbalance, proved most effective for separating AF fromNo-AF cases (sensitivity = 0.74, specificity = 0.70, C-index = 0.80). Our modelis the first machine learning based method for identification of AF cases inHCM. This model demonstrates good performance, addresses data imbalance, andsuggests that AF is associated with a more severe cardiac HCM phenotype.

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Machine Learning Methods for Identifying Atrial Fibrillation Cases and  Their Predictors in Patients With Hypertrophic Cardiomyopathy: The  HCM-AF-Risk Model