Zewei Chen1, Xin Zhang1, Zhuoyong Zhang2. 1. Department of Chemistry, Capital Normal University, 105 West 3rd Ring Road North, Beijing, 100048, China. 2. Department of Chemistry, Capital Normal University, 105 West 3rd Ring Road North, Beijing, 100048, China. gusto2008@vip.sina.com.
Abstract
PURPOSE: Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring. METHODS: Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD. RESULTS: For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models. CONCLUSIONS: Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.
PURPOSE: Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring. METHODS: Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD. RESULTS: For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models. CONCLUSIONS: Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.
Authors: Kaj Metsärinne; Anders Bröijersen; Ilkka Kantola; Leo Niskanen; Aila Rissanen; Tina Appelroth; Nora Pöntynen; Tuija Poussa; Veikko Koivisto; Antti Virkamäki Journal: Prim Care Diabetes Date: 2014-07-21 Impact factor: 2.459
Authors: Alfonso M Cueto-Manzano; Laura Cortés-Sanabria; Héctor R Martínez-Ramírez; Enrique Rojas-Campos; Benjamin Gómez-Navarro; Marcelo Castillero-Manzano Journal: Arch Med Res Date: 2014-07-01 Impact factor: 2.235
Authors: Maryam Afkarian; Michael C Sachs; Bryan Kestenbaum; Irl B Hirsch; Katherine R Tuttle; Jonathan Himmelfarb; Ian H de Boer Journal: J Am Soc Nephrol Date: 2013-01-29 Impact factor: 10.121