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In Echocardiography (Mount Kisco, N.Y.) ; h5-index 0.0

A method of analysis of a database of patients (n = 10 329) screened for an abdominal aortic aneurysm (AAA) is presented. Self-reported height, weight, age, gender, ethnicity, and parameters "Heart Problems," "Hypertension," "High Cholesterol," "Diabetes Mellitus," "Smoker Past 2 Years," "Ever Smoked?," "Family History AAA," and "Family History Brain Aneurysm" were provided. Incidence of a AAA (defined as 3 cm diameter) was calculated as a function of age and body mass index (BMI) of greater than or less than a BMI 25 for various patient groups. Age was grouped into one of three categories in 15-year intervals (35-50 years, 50-65 years, and 65 to 80 years). Most patients were Caucasian (n = 8575) and the largest group of patients with a AAA was the Caucasian male (198 of 279 total detected AAAs). A machine learning algorithm was written, with learning inputs from the acquired patient database. Of all groups, Caucasian males were found to have the highest incidence of AAA, with males in general higher than females. Smoking within the past two years was highly associated with AAA incidence, and a past history of smoking to a lesser extent. The incidence of AAA increased with age. When dividing groups into two cohorts by a BMI of 25, generally middle-aged patients with a BMI > 25 had a higher incidence of a AAA. However, in general, the older age group with a BMI < 25 had a higher incidence of AAA. The addition of machine learning allows one to note the effect of an input keeping other input parameters constant. This helps identify a parameter that may be an independent predictor of a particular outcome. When using BMI as the single changing input, an increasing BMI was associated with an increased probability of a AAA, most significantly in middle-aged patients, and then narrowing to similar probabilities in older age. This AAA screening program is ongoing. As data continues to be collected with particularly those patient groups presently underrepresented, questions as to an association of AAA with BMI as a function of age, and also an improvement in machine learning algorithm accuracy for various patient populations will continue.

Kerut Edmund Kenneth, To Filip, Summers Kelli L, Sheahan Claudie, Sheahan Malachi


artificial intelligence, machine learning, sensitivity analysis