Artículo

An Intelligent Model for Improving Risk Assessment in Sterilization Units Using Revised FMEA, Fuzzy Inference, k-Nearest Neighbors and Support Vector Machine.

En Naaoui, Amine; Gallab, Maryam; Kaicer, Mohammed

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology y cosechado de y cosechado de Revistas UNAM

Licencia de uso

La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx. Ver términos de la licencia

Procedencia del contenido

Cita

En Naaoui, Amine, et al. (2023). An Intelligent Model for Improving Risk Assessment in Sterilization Units Using Revised FMEA, Fuzzy Inference, k-Nearest Neighbors and Support Vector Machine.. Journal of Applied Research and Technology; Vol. 21 Núm. 5, 2023; 772-786. Recuperado de https://repositorio.unam.mx/contenidos/4149302

Descripción del recurso

Autor(es)
En Naaoui, Amine; Gallab, Maryam; Kaicer, Mohammed
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
An Intelligent Model for Improving Risk Assessment in Sterilization Units Using Revised FMEA, Fuzzy Inference, k-Nearest Neighbors and Support Vector Machine.
Fecha
2023-10-30
Resumen
The complex environment of the hospital and the critical operations practiced in the medical departments, such as the sterili zation unit, require implementing risk assessment plans as fundamental support for effective management. The Failure Modes Analysis and Effects (FMEA) method is one of the popular methods used to perform the risk assessment process. Fuzzy logic and machine learning techniques provide robust devices that improve the efficiency of several risk assessment methods, such as FMEA. Hence, this study aims to enhance the efficiency of risk assessment in hospital sterilization units using an intelligent model based on revised FMEA, an improved FMEA adaptable to the studied system, fuzzy inference system, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) techniques. An interesting application of the model in the central sterilization unit of the largest university hospital is presented. The performance of the proposed model is evaluated at the end to prove its efficiency.
Tema
Risk Assessment; Fmea; Fuzzy Inference System; Support Vector Machine; K-nearest Neighbor; Sterilization Unit
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces