Constructing a prediction model for physiological parameters for malnutrition in hemodialysis patients.
Sci Rep
; 9(1): 10767, 2019 07 24.
Article
em En
| MEDLINE
| ID: mdl-31341234
ABSTRACT
A retrospective analysis of the improvement in the health condition of patients undergoing hemodialysis was done to understand the important factors that can affect malnutrition in these patients. In this study, data from patients who underwent hemodialysis between 2010 and 2015 in a regional hospital in Yunlin County were collected from the Taiwan Society of Nephrology-Kidney Transplantation database. A total of 1049 medical records from 300 patients with age over 20 and underwent hemodialysis were collected for this study. A decision tree C5.0 and logistic regression were used to identify 40 independent variables, as well as the association of the dependent variable albumin. Then, the C5.0 decision tree, logistic regression, and support vector machine (SVM) methods were applied to find a combination of factors that contributed to malnutrition in patients undergoing hemodialysis. Predictive models were established. Finally, a receiver operating characteristic curve and confusion matrix was used to evaluate the standard of performance of these models. All analytical methods indicated that "age" was an important factor. In particular, the best predictive model was the SVM-model 4, with a training accuracy rate of 98.95% and test accuracy rate of 66.89%, identified that "age" and 15 other important factors were the most related to hemodialysis. The findings of this study can be used as a reference for clinical applications.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
6_ODS3_enfermedades_notrasmisibles
Problema de saúde:
6_malnutrition_nutritional_deficiencies
Assunto principal:
Diálise Renal
/
Desnutrição
Tipo de estudo:
Etiology_studies
/
Observational_studies
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Prognostic_studies
/
Risk_factors_studies
Aspecto:
Determinantes_sociais_saude
Limite:
Aged
/
Female
/
Humans
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Male
/
Middle aged
País/Região como assunto:
Asia
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Taiwan