title: Using machine learning methods for predicting inhospital mortality in patients undergoing open repair of abdominal aortic aneurysm.
abstract: An abdominal aortic aneurysm is an abnormal dilatation of the aortic vessel at abdominal level. This disease presents high rate of mortality and complications causing a decrease in the quality of life and increasing the cost of treatment. To estimate the mortality risk of patients undergoing surgery is complex due to the variables associated. The use of clinical decision support systems based on machine learning could help medical staff to improve the results of surgery and get a better understanding of the disease. In this work, the authors present a predictive system of inhospital mortality in patients who were undergoing to open repair of abdominal aortic aneurysm. Different methods as multilayer perceptron, radial basis function and Bayesian networks are used. Results are measured in terms of accuracy, sensitivity and specificity of the classifiers, achieving an accuracy higher than 95%. The developing of a system based on the algorithms tested can be useful for medical staff in order to make a better planning of care and reducing undesirable surgery results and the cost of the post-surgical treatments.
authors.affiliation: Bio-inspired Engineering and Health Computing Research Group, IBIS, University of Alicante, Spain.
authors.forename: Ana
authors.initials: A
authors.lastname: Monsalve-Torra
authors.affiliation: Department of Computer Technology of the University of Alicante, Spain. Electronic address: druiz@dtic.ua.es.
authors.forename: Daniel
authors.initials: D
authors.lastname: Ruiz-Fernandez
authors.affiliation: Bio-inspired Engineering and Health Computing Research Group, IBIS, University of Alicante, Spain.
authors.forename: Oscar
authors.initials: O
authors.lastname: Marin-Alonso
authors.affiliation: Department of Computer Technology of the University of Alicante, Spain.
authors.forename: Antonio
authors.initials: A
authors.lastname: Soriano-Payá
authors.affiliation: Departamento de cirugía cardiovascular - Fundación Cardioinfantil- Instituto de Cardiología, Bogotá, Colombia.
authors.forename: Jaime
authors.initials: J
authors.lastname: Camacho-Mackenzie
authors.affiliation: Departamento de cirugía cardiovascular - Fundación Cardioinfantil- Instituto de Cardiología, Bogotá, Colombia.
authors.forename: Marisol
authors.initials: M
authors.lastname: Carreño-Jaimes
mesh_terms: D017544:Aortic Aneurysm, Abdominal / Q000401:mortality / Q000601:surgery*; D001499:Bayes Theorem; D017052:Hospital Mortality; D006801:Humans; D000069550:Machine Learning; D011788:Quality of Life; D018570:Risk Assessment; D012307:Risk Factors; D016896:Treatment Outcome
keywords: Abdominal aortic aneurysm; Clinical decision support system; Data analysis; Machine learning; Mortality prediction