Sabrina Mezzatesta1, Claudia Torino2, Pasquale De Meo3, Giacomo Fiumara1, Antonio Vilasi4. 1. Department of Mathematics and Computer Science, Physical Sciences and Earth Sciences, University of Messina, Messina, Italy. 2. Institute of Clinical Physiology - Reggio Calabria Unit, Laboratory of Bioinformatics, National Research Council, Italy. 3. Department of Ancient and Modern Civilizations, University of Messina, Messina, Italy. 4. Institute of Clinical Physiology - Reggio Calabria Unit, Laboratory of Bioinformatics, National Research Council, Italy. Electronic address: avilasi@ifc.cnr.it.
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.
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.
Authors: Mira Kim; Kyunghee Chae; Seungwoo Lee; Hong-Jun Jang; Sukil Kim Journal: Int J Environ Res Public Health Date: 2020-12-17 Impact factor: 3.390
Authors: Cheng-Chien Lai; Wei-Hsin Huang; Betty Chia-Chen Chang; Lee-Ching Hwang Journal: Int J Environ Res Public Health Date: 2021-03-05 Impact factor: 3.390
Authors: Sheetal Chaudhuri; Andrew Long; Hanjie Zhang; Caitlin Monaghan; John W Larkin; Peter Kotanko; Shashi Kalaskar; Jeroen P Kooman; Frank M van der Sande; Franklin W Maddux; Len A Usvyat Journal: Semin Dial Date: 2020-09-13 Impact factor: 3.455