From retina to heart: explainable machine learning using OCT and Clinical covariates for heart failure screening

Heart failure affects over 64 million individuals globally, contributing to elevated mortality rates and substantial healthcare costs. This study investigates the potential of retinal optical coherence tomography features combined with routine clinical variables as biomarkers for the detection of heart failure, exploring a potential avenue for improved risk assessment and screening support using explainable machine-learning tools. A comprehensive dataset of normal and heart failure patients' demographic and medical records including retinal measurements from both eyes was used. Among the machine learning models employed, the Extreme Gradient Boosting model demonstrated the best performance, achieving an accuracy of 73.31%, a precision of 71.81%, and an area under the receiver operating characteristic curve of 0.837. Explainability analyses further revealed that macular thickness metrics, particularly in the inner temporal subfield, inner nasal subfield, and outer superior subfields of the left eye, along with key clinical indicators such as age, body mass index, and glycated hemoglobin, were the most influential predictors of heart failure status. Local explanation methods also provided patient-level reasoning consistent with overall cohort patterns. To our knowledge, this is the first study to use an integrated, explainable approach incorporating bilateral retinal optical coherence tomography measurements with routine clinical indicators for heart failure detection, providing an interpretable and accessible alternative to black-box models while helping address the cost, invasiveness, and limited accessibility of existing heart failure diagnostic tools.