A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis. (August 2019)

• Record Type: Journal Article
• Authors: Mezzatesta, Sabrina; Torino, Claudia; Meo, Pasquale De; Fiumara, Giacomo; Vilasi, Antonio
• Is Part Of: Computer methods and programs in biomedicine. Volume 177(2019)
• Page Start: 9
• Page End: 15
• Publication Date: 2019-08
• Subjects: Machine learning -- Cardiovascular outcomes -- ESRD -- Prognosis; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.05.005
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract: Background and Objective: Patients with End- Stage Kidney Disease (ESKD) have a unique cardiovascular risk. This study aims at predicting, with a certain precision, death and cardiovascular diseases in dialysis patients. Methods: To achieve our aim, machine learning techniques have been used. Two datasets have been taken into consideration: the first is an Italian dataset obtained from the Istituto di Fisiologia Clinica of Consiglio Nazionale delle Ricerche of Reggio Calabria; the second is an American dataset provided by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) repository. From each one we obtained 5 datasets, according to the outcome of interest. We tested different types of algorithm (both linear and non-linear), but the final choice was to use Support Vector Machine. In particular, we obtained the best performances using the non-linear SVC with RBF kernel algorithm, optimizing it with GridSearch. The last is an algorithm useful to search the best combination of hyper-parameters (in our case, to find the best couple (C, γ )), in order to improve the accuracy of the algorithm. Results: The use of non-linear SVC with RBF kernel algorithm, optimized with GridSearch, allowed to obtain an accuracy of 95.25% in the Italian dataset and of 92.15% in the American dataset, in a timeframe of 2.5 years, in the prediction of Ischaemic Heart Disease. A worse performance was obtained for the other outcomes. Conclusions: The machine learning-based approach applied in our study is able to predict, with a high accuracy, the outbreak of cardiovascular diseases in patients on dialysis.