Identify Cancer Patients at Risk for Heart Failure using Electronic Health Record and Genetic Data

Abstract

Heart failure is a critical side effect of many cancer treatments. Identifying cancer patients at a high risk of cardiotoxicity before cancer treatment is a critical step towards early detection and possible prevention. This study seeks to examine how genetic data can be used with Electronic Health Record (EHR) data to identify cancer patients at risk of treatment-related heart failure. We explored four machine learning models, including Logistic Regression (LR), Support Vector Machines (SVMs), Random Forest (RF), and Gradient Boost (GB) for heart failure prediction using EHR data linked with genetic data from the UK Biobank. We first identified the best machine learning model using only EHR data and then further added genetic data to the model using three different strategies. The experimental results show that the GB model combining EHR data and genetic data achieved the best area under the curve (AUC) score of 0.7781, outperforming the machine learning models using only EHR data. Among the three strategies of including genetic data, the genome-wide association study (GWAS) method achieved the best performance. Our study shows that genomic data can be used to improve the performance of heart failure prediction among cancer patients.