TY - GEN
T1 - Development of a predictor software to determine the periodicity of patient monitors preventive maintenance in the intensive care unit of a health care institution
AU - Silva, Juan Pablo Vargas
AU - Salas, Carlos Julian Lopez
AU - Cordero, Mario Fernando Morales
AU - Forero, Lusvin Javier Amado
AU - Pereira, Carlos Julio Arizmendi
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/25
Y1 - 2021/8/25
N2 - This project seeks to develop a software predictor to determine the amount of annual maintenance that a patient monitor (PM) will have in order to adjust the periodicity of preventive maintenance for such equipment. To achieve this, a database is created with which the predictor system is trained using regression methodologies such as: multinomial logistic regression, linear regression and stepwise regression. Principal component analysis (PCA) and feature selection by neighborhood component analysis for regression (FSRNCA) are implemented as dimensionality reduction methods. For software coding, Matlab and Python programming languages are used, and the stepwise regression methodology is implemented as a predictor model together with the PCA dimensionality reduction method, obtaining a success rate of approximately 65% with a standard deviation of approximately ±24.
AB - This project seeks to develop a software predictor to determine the amount of annual maintenance that a patient monitor (PM) will have in order to adjust the periodicity of preventive maintenance for such equipment. To achieve this, a database is created with which the predictor system is trained using regression methodologies such as: multinomial logistic regression, linear regression and stepwise regression. Principal component analysis (PCA) and feature selection by neighborhood component analysis for regression (FSRNCA) are implemented as dimensionality reduction methods. For software coding, Matlab and Python programming languages are used, and the stepwise regression methodology is implemented as a predictor model together with the PCA dimensionality reduction method, obtaining a success rate of approximately 65% with a standard deviation of approximately ±24.
KW - Machine Learning
KW - Patient Monitor
KW - Regression Analysis
UR - http://www.scopus.com/inward/record.url?scp=85116464270&partnerID=8YFLogxK
U2 - 10.1109/SCLA53004.2021.9540072
DO - 10.1109/SCLA53004.2021.9540072
M3 - Libros de Investigación
AN - SCOPUS:85116464270
T3 - 2021 2nd Sustainable Cities Latin America Conference, SCLA 2021
BT - 2021 2nd Sustainable Cities Latin America Conference, SCLA 2021
A2 - Beingolea, Jorge R.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd Sustainable Cities Latin America Conference, SCLA 2021
Y2 - 25 August 2021 through 27 August 2021
ER -
