Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning - Mehdi Tavafi; Kalpdrum Passi; Robert Ohle (Algorithms)
This study explores machine learning’s potential for early Acute Aortic Syndrome (AAS) prediction by integrating and cleaning extensive clinical datasets from 68 emergency departments in the USA, covering the medical histories of nearly 150,000 patients from 2021 to 2022. Utilizing various data-splitting strategies and classifiers, the research constructs predictive models and addresses dataset size limitations, achieving an exceptional accuracy of 99.3% with the Relief feature method and random forest classifier, facilitating further research on AAS and other cardiovascular diseases.