Early risk prediction in acute aortic syndrome on clinical data using machine learning

Abstract

Advancements in machine learning present novel opportunities for early prediction of Acute Aortic Syndrome (AAS) as a critical and life-threatening clinical condition and the identification of critical features influencing this prediction. This study concentrates on integrating, cleaning, and handling missing data from extensive clinical datasets sourced from 150 emergency departments across Canada and the USA. Covering medical histories of nearly 150,000 patients from 2021 to 2022, the dataset comprises categorical clinical variables. Additionally, the research focuses on constructing predictive machine learning models utilizing various data-splitting strategies and classifiers to optimize AAS prediction. Methodologically, the study encompasses data identification, acquisition, exploration, processing, and feature extraction, followed by dimensionality reduction using Principal Component Analysis (PCA) and other feature selection methods such as Correlation-based (CFS) and Relief. The multiple imputations method and the SMOTE method are utilized for handling missing and imbalanced data, respectively. The findings demonstrate that employing the Relief-feature method with an 80-10-10 split ratio alongside the Random Forest classifier yields an exceptional accuracy of 99.3%, surpassing alternative models.. Furthermore, this research addresses a prevalent challenge encountered by many researchers regarding dataset size limitations, thereby facilitating the utilization of the integrated and prepared dataset for research on AAS and other cardiovascular diseases.