title: Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models.
abstract: OBJECTIVE
  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.affiliation: Department of Chemistry, Capital Normal University, 105 West 3rd Ring Road North, Beijing, 100048, China.
authors.forename: Zewei
authors.initials: Z
authors.lastname: Chen
authors.affiliation: Department of Chemistry, Capital Normal University, 105 West 3rd Ring Road North, Beijing, 100048, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Chemistry, Capital Normal University, 105 West 3rd Ring Road North, Beijing, 100048, China. gusto2008@vip.sina.com.
authors.forename: Zhuoyong
authors.identifier: http://orcid.org/0000-0001-8224-1788
authors.initials: Z
authors.lastname: Zhang
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D002681:China / Q000453:epidemiology; D018450:Disease Progression; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D015999:Multivariate Analysis; D051436:Renal Insufficiency, Chronic / Q000175:diagnosis / Q000453:epidemiology / Q000503:physiopathology; D018570:Risk Assessment / Q000379:methods / Q000706:statistics & numerical data; D063189:Symptom Assessment / Q000379:methods
keywords: Chronic kidney disease (CKD); Clinical screening; Multivariate models; Risk assessment