Using data mining techniques to determine whether to outsource medical equipment maintenance tasks in real contexts

Antonio Miguel-Cruz, Pedro Antonio Aya-Parra, William Ricardo Rodríguez-Dueñas, Andres Felipe Camelo-Ocampo, Viena Sofia Plata-Guao, Hector H. Correal O., Nidia Patricia Córdoba-Hernández, Angelmiro Nuñez-Cruz, Jefferson S. Sarmiento-Rojas, Daniel Alejandro Quiroga-Torres

Resultado de la investigación: Contribución a una revistaArtículo de la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

The purpose of this study was to determine whether the maintenance of medical equipment should be outsourced (or not). For this, we used data mining techniques called decision trees. We (1) collected 2364 maintenance works orders from 62 medical devices installed in a 900-bed hospital; (2) then we randomly selected 90% of the maintenance works orders to train 8 different decision tree schemas (J48 (pruned and unpruned), Naive Bayes tree, random tree, alternating decision tree, logistic model tree, decision stump, REP tree); (3) next, the remaining 10% of the works orders were used to test the decision tree schemas. The relative absolute error was used to evaluate what the tested decision tree schemas had learned; finally (4), we chose the decision tree schema with the lowest relative absolute error. Overall, the decision tree schemas performed well. 62.5% (5/8) of the decision tree schemas had less than 20% relative absolute error. 87.5% (7/8) of the decision tree schemas had more than 90% in the correct classification (whether to outsource maintenance tasks or not). The different tested decision tree schemas showed that the most important variables when making the decision whether to outsource maintenance tasks or not were: medical device, risk class (I, IIA, IIB, III), complexity, obsolescence, maintenance frequency, service time and outsourcing. The best decision tree schema was the logistic model tree (LMT) with 14.6628% relative absolute error and 94.7034% in the correct classification.

Idioma originalInglés estadounidense
Páginas (desde-hasta)295-298
Número de páginas4
PublicaciónIFMBE Proceedings
Volumen68
N.º3
DOI
EstadoPublicada - may 30 2018
EventoWorld Congress on Medical Physics and Biomedical Engineering, WC 2018 - Prague, República Checa
Duración: jun 3 2018jun 8 2018

All Science Journal Classification (ASJC) codes

  • Bioingeniería
  • Ingeniería biomédica

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