Machine Learning-Based Prediction of Abdominal Aortic Aneurysms for Individualized Patient Care

Kelli L. Summers, LSU Health Sciences Center - New Orleans
Edmund K. Kerut, LSU Health Sciences Center - New Orleans
Filip To, Mississippi State University
Claudie M. Sheahan, LSU Health Sciences Center - New Orleans
Malachi G. Sheahan, LSU Health Sciences Center - New Orleans


OBJECTIVE: The United States Preventative Services Task Force (USPSTF) guidelines for screening for abdominal aortic aneurysms (AAA) are broad and exclude many at risk groups. We analyzed a large AAA screening database to examine the utility of a novel machine learning (ML) model for predicting individual risk of AAA. METHODS: We created a ML model to predict the presence of AAAs (>3cm) from the database of a national non-profit screening organization (AAAneurysm Outreach). Participants self-reported demographics and co-morbidities. The model is a two-layered feed-forward shallow network. The ML model then generated AAA probability based on patient characteristics. We evaluated graphs to determine significant factors, and then compared those graphs to a traditional logistic regression model. RESULTS: We analyzed a patient cohort of 10,033 subjects with an AAA prevalence of 2.74%. Consistent with logistic regression analysis, the ML model identified the following predictors of AAA: Caucasian race, male gender, increasing age, and recent or past smoker with recent smoker having a more profound affect (P < .05). Interestingly, the ML model showed BMI was associated with likelihood of AAAs, especially for younger females. The ML model also identified a higher than predicted risk of AAA in several groups including female non-smokers with cardiac disease, female diabetics, those with a family history of AAA, and those with hypertension or hyperlipidemia at older ages. An elevated BMI conveyed a higher than expected risk in male smokers and all females. The ML model also identified a complex relationship of both diabetes mellitus and hyperlipidemia with gender. Family history of AAA was a more important risk factor in the ML model for both men and women too. CONCLUSIONS: We successfully developed an ML model based on an AAA screening database that unveils a complex relationship between AAA prevalence and many risk factors, including BMI. The model also highlights the need to expand AAA screening efforts in women. Using ML models in the clinical setting has the potential to deliver precise, individualized screening recommendations.