Diagnosing hypertrophic cardiomyopathy using machine learning models on CMRs and EKGs of the heart

Hypertrophic cardiomyopathy (HCM) is a common inherited heart disorder manifesting as hypertrophy of the left ventricle of the heart that often goes undiagnosed. It is imperative that hypertrophic cardiomyopathy is diagnosed early since there is a possibility of sudden cardiac death (SCD) as a result of HCM. In this study, we created a pair of models, one convolutional neural network (CNN) model and one Long Short-Term Memory (LSTM) model, that are capable of classifying cardiac magnetic resonance (CMR) and heart electrocardiogram (EKG) scans, respectively. Each of these models classified their respective scans into HCM and non-HCM categories. The CNN model had an accuracy of 94.71%, a precision of 96.97%, a recall of 91.21%, and an F1 score of 94.85%. The LSTM model had an accuracy of 90.51%, a precision of 60.31%, a recall of 60.08%, and an F1 score of 60.19%. These results showed that these machine learning models are viable tools that could assist physicians in the diagnosis of HCM patients.