Explainable machine learning predictions to support personalized cardiology strategies.

ABSTRACT:

Aims

A widely practiced intervention to modify cardiac health, the effect of physical activity on older adults is likely heterogeneous. While machine learning (ML) models that combine various systemic signals may aid in predictive modelling, the inability to rationalize predictions at a patient personalized level is a major shortcoming in the current field of ML.

Methods and results

We applied a novel methodology, SHapley Additive exPlanations (SHAP), on a dataset of older adults n = 86 (mean age 72 ± 4 years) whose physical activity levels were studied alongside changes in their left ventricular (LV) structure. SHAP was tested to provide intelligible visualization on the magnitude of the impact of the features in their physical activity levels on their LV structure.

SUBMITTER: Loh R 

PROVIDER: S-EPMC9708009 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature