Fernández-Lozano, CarlosAlonso Valente, RafaelFidalgo Díaz, ManuelPazos, A.2019-01-152019-01-152018Fernández-Lozano C, Alonso Valente R, Fidalgo Díaz M, Pazos A. A generalized linear model for cardiovascular complications prediction in PD patients. proceedings of the First International Conference on Data Science, E-learning and Information Systems; 2018 Oct 1-2; Madrid, Spain. New York, NY: Association for Computer Machinery; 2018978-1-4503-6536-9http://hdl.handle.net/2183/21591[Abstract] This study was conducted using machine learning models to identify patient non-invasive information for cardiovascular complications prediction in peritoneal dialysis patients. Nowadays is well known that cardiovascular diseases are the key to mortality in patients undergoing peritoneal dialysis as the risk of cardiovascular disease increases with the progression of renal failure. Primary aim is to establish variables most associated with cardiovascular complications. To achieve this goal four different machine learning techniques were used. We found that the best classification algorithm was a Generalized Linear Model, which achieved AUC values above 96% using a small subset of the original variables following a feature selection approach. Our approach allows us to increase the interpretability of the combinations of traditional factors, advanced chronic kidney disease factors and peritoneal dialysis factors all related with cardiovascular risk profile. The final model is based primarily in the traditional factors.engPeritoneal dialysisMachine learningCardiovascular risk predictionFeature selectionGimnetconference outputopen access