TY - GEN
T1 - Principal Components and Neural Networks Based Linear Regression to Determine Biomedical Equipment Maintenance Cost in the Peruvian Social Security Health System
AU - Toledo, E.
AU - de la Cruz, C.
AU - Mamani, C.
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - In this study, multivariate linear regression models and principal component analysis, and artificial neural networks (ANN) were designed to predict the monthly cost of biomedical equipment maintenance services in the Peruvian Social Health Insurance (EsSalud). The data employed in the development of these models were obtained from maintenance contracts and their execution, from 2019 to present. The results demonstrate that the multivariable linear regression model acquires adequate metrics; still, such a model has four correlated variables. Hence, the use of the principal component regression model enhanced the outcomes by using two components, thus acquiring greater interpretability. Finally, the ANN model obtained the best performance predictor.
AB - In this study, multivariate linear regression models and principal component analysis, and artificial neural networks (ANN) were designed to predict the monthly cost of biomedical equipment maintenance services in the Peruvian Social Health Insurance (EsSalud). The data employed in the development of these models were obtained from maintenance contracts and their execution, from 2019 to present. The results demonstrate that the multivariable linear regression model acquires adequate metrics; still, such a model has four correlated variables. Hence, the use of the principal component regression model enhanced the outcomes by using two components, thus acquiring greater interpretability. Finally, the ANN model obtained the best performance predictor.
KW - Artificial neural network
KW - Equipment maintenance cost
KW - Linear regression
KW - Machine learning
KW - Principal components regression
UR - http://www.scopus.com/inward/record.url?scp=85184286364&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-49410-9_4
DO - 10.1007/978-3-031-49410-9_4
M3 - Conference contribution
AN - SCOPUS:85184286364
SN - 9783031494093
T3 - IFMBE Proceedings
SP - 31
EP - 42
BT - IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering - Proceedings of CLAIB and CBEB 2022, Volume 4
A2 - Marques, Jefferson Luiz
A2 - Rodrigues, Cesar Ramos
A2 - Suzuki, Daniela Ota
A2 - García Ojeda, Renato
A2 - Marino Neto, José
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th Latin American Congress on Biomedical Engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical Engineering, CBEB 2022
Y2 - 24 October 2022 through 28 October 2022
ER -