Competitive evaluation of data mining algorithms for use in classification of leukocyte subtypes with Raman microspectroscopy.
Analyst
; 140(7): 2473-81, 2015 Apr 07.
Article
em En
| MEDLINE
| ID: mdl-25584436
ABSTRACT
Raman microspectroscopy has been investigated for some time for use in label-free cell sorting devices. These approaches require coupling of the Raman spectrometer to complex data mining algorithms for identification of cellular subtypes such as the leukocyte subpopulations of lymphocytes and monocytes. In this study, three distinct multivariate classification approaches, (PCA-LDA, SVMs and Random Forests) are developed and tested on their ability to classify the cellular subtype in extracted peripheral blood mononuclear cells (T-cell lymphocytes from myeloid cells), and are evaluated in terms of their respective classification performance. A strategy for optimisation of each of the classification algorithm is presented with emphasis on reduction of model complexity in each of the algorithms. The relative classification performance and performance characteristics are highlighted, overall suggesting the radial basis function SVM as a robust option for classification of leukocytes with Raman microspectroscopy.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Análise Espectral Raman
/
Algoritmos
/
Mineração de Dados
/
Leucócitos
Tipo de estudo:
Evaluation_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Analyst
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
Irlanda