Developing machine learning algorithms to predict outcomes following vascular surgery using the Vascular Quality Initiative database

Abstract

Machine learning (ML) is a rapidly advancing technology that enables computers to leverage large amounts of data and make accurate predictions by learning complex relationships between inputs (e.g., patient characteristics) and outputs (e.g., health outcomes)1. This field has been driven by the explosion of electronic information combined with increasing computation power1. Traditional statistical techniques such as logistic regression are often limited by the linearity assumption, which assumes a linear relationship between the log value of the outcome and the covariates2.

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