Dose-effect relationship analysis of TCM based on deep Boltzmann machine and partial least squares.
Math Biosci Eng
; 20(8): 14395-14413, 2023 06 30.
Article
en En
| MEDLINE
| ID: mdl-37679141
A dose-effect relationship analysis of traditional Chinese Medicine (TCM) is crucial to the modernization of TCM. However, due to the complex and nonlinear nature of TCM data, such as multicollinearity, it can be challenging to conduct a dose-effect relationship analysis. Partial least squares can be applied to multicollinearity data, but its internally extracted principal components cannot adequately express the nonlinear characteristics of TCM data. To address this issue, this paper proposes an analytical model based on a deep Boltzmann machine (DBM) and partial least squares. The model uses the DBM to extract nonlinear features from the feature space, replaces the components in partial least squares, and performs a multiple linear regression. Ultimately, this model is suitable for analyzing the dose-effect relationship of TCM. The model was evaluated using experimental data from Ma Xing Shi Gan Decoction and datasets from the UCI Machine Learning Repository. The experimental results demonstrate that the prediction accuracy of the model based on the DBM and partial least squares method is on average 10% higher than that of existing methods.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Automático
/
Medicina Tradicional China
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Math Biosci Eng
Año:
2023
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Estados Unidos