Application of data mining techniques to explore predictors of upper urinary tract damage in patients with neurogenic bladder
Braz. j. med. biol. res
;
50(10): e6638, 2017. tab, graf
Artículo
en Inglés
| LILACS
| ID: biblio-888941
ABSTRACT
This study proposed a decision tree model to screen upper urinary tract damage (UUTD) for patients with neurogenic bladder (NGB). Thirty-four NGB patients with UUTD were recruited in the case group, while 78 without UUTD were included in the control group. A decision tree method, classification and regression tree (CART), was then applied to develop the model in which UUTD was used as a dependent variable and history of urinary tract infections, bladder management, conservative treatment, and urodynamic findings were used as independent variables. The urethra function factor was found to be the primary screening information of patients and treated as the root node of the tree; Pabd max (maximum abdominal pressure, >14 cmH2O), Pves max (maximum intravesical pressure, ≤89 cmH2O), and gender (female) were also variables associated with UUTD. The accuracy of the proposed model was 84.8%, and the area under curve was 0.901 (95%CI=0.844-0.958), suggesting that the decision tree model might provide a new and convenient way to screen UUTD for NGB patients in both undeveloped and developing areas.
Texto completo:
Disponible
Índice:
LILACS (Américas)
Asunto principal:
Sistema Urinario
/
Vejiga Urinaria Neurogénica
/
Minería de Datos
Tipo de estudio:
Estudio observacional
/
Estudio pronóstico
/
Factores de riesgo
Límite:
Femenino
/
Humanos
/
Masculino
Idioma:
Inglés
Revista:
Braz. j. med. biol. res
Asunto de la revista:
Biologia
/
Medicina
Año:
2017
Tipo del documento:
Artículo
/
Documento de proyecto
País de afiliación:
China
Institución/País de afiliación:
Southern Medical University/CN
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