  Cardiotoxicity related to cancer therapies has become a serious issue,
diminishing cancer treatment outcomes and quality of life. Early detection of
cancer patients at risk for cardiotoxicity before cardiotoxic treatments and
providing preventive measures are potential solutions to improve cancer
patients's quality of life. This study focuses on predicting the development of
heart failure in cancer patients after cancer diagnoses using historical
electronic health record (EHR) data. We examined four machine learning
algorithms using 143,199 cancer patients from the University of Florida Health
(UF Health) Integrated Data Repository (IDR). We identified a total number of
1,958 qualified cases and matched them to 15,488 controls by gender, age, race,
and major cancer type. Two feature encoding strategies were compared to encode
variables as machine learning features. The gradient boosting (GB) based model
achieved the best AUC score of 0.9077 (with a sensitivity of 0.8520 and a
specificity of 0.8138), outperforming other machine learning methods. We also
looked into the subgroup of cancer patients with exposure to chemotherapy drugs
and observed a lower specificity score (0.7089). The experimental results show
that machine learning methods are able to capture clinical factors that are
known to be associated with heart failure and that it is feasible to use
machine learning methods to identify cancer patients at risk for cancer
therapy-related heart failure.
