Prediction of the Probability and Risk Factors of Early Abdominal Aortic Aneurysm Using the Gradient Boosted Decision Trees Model

Abstract

Currently, abdominal aortic aneurysm (AAA) diagnosis mainly relies on the analysis of the image data, such as Doppler ultrasonic and computed tomography (CT). Once AAA has formed, it may rupture and lead to death at any time. Surgical or endovascular treatment was the only method, but it has a high complication rate and poses a huge economic burden to patients. The gradient boosted decision trees (GBDT) model proposed in this paper is used to predict the probability and risk factors that lead to AAA, and the prediction accuracy of the algorithm is able to reach as high as 96%. This study selected 15 related AAA features as training samples. After the training, age, triglycerides (TG), blood pressure (BP), low-density lipoprotein cholesterol (LDL-C), blood glucose (Glu), and body mass index (BMI) are found to have a direct impact on AAA. For individuals with a high AAA probability, the risk factors that contribute the most to the AAA probability can be determined with the GBDT model. This study presents the GBDT model that effectively predicts the probability and risk factors of early AAA, which enables an early intervention and control of these risk factors against incidence of AAA.