A neural network - based algorithm for predicting stone -free status after ESWL therapy
Int. braz. j. urol
;
43(6): 1110-1114, Nov.-Dec. 2017. tab, graf
Artículo
en Inglés
| LILACS
| ID: biblio-892913
ABSTRACT
ABSTRACT Objective:
The prototype artificial neural network (ANN) model was developed using data from patients with renal stone, in order to predict stone-free status and to help in planning treatment with Extracorporeal Shock Wave Lithotripsy (ESWL) for kidney stones. Materials andMethods:
Data were collected from the 203 patients including gender, single or multiple nature of the stone, location of the stone, infundibulopelvic angle primary or secondary nature of the stone, status of hydronephrosis, stone size after ESWL, age, size, skin to stone distance, stone density and creatinine, for eleven variables. Regression analysis and the ANN method were applied to predict treatment success using the same series of data.Results:
Subsequently, patients were divided into three groups by neural network software, in order to implement the ANN training group (n=139), validation group (n=32), and the test group (n=32). ANN analysis demonstrated that the prediction accuracy of the stone-free rate was 99.25% in the training group, 85.48% in the validation group, and 88.70% in the test group.Conclusions:
Successful results were obtained to predict the stone-free rate, with the help of the ANN model designed by using a series of data collected from real patients in whom ESWL was implemented to help in planning treatment for kidney stones.
Texto completo:
Disponible
Índice:
LILACS (Américas)
Asunto principal:
Algoritmos
/
Litotricia
/
Cálculos Renales
/
Redes Neurales de la Computación
Tipo de estudio:
Estudio diagnóstico
/
Estudio pronóstico
/
Factores de riesgo
Límite:
Adolescente
/
Adulto
/
Anciano
/
Niño
/
Child, preschool
/
Femenino
/
Humanos
/
Lactante
/
Masculino
Idioma:
Inglés
Revista:
Int. braz. j. urol
Asunto de la revista:
Urología
Año:
2017
Tipo del documento:
Artículo
País de afiliación:
Turquía
Institución/País de afiliación:
Gaziantep University/TR
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