A frequency based encoding technique for transformation of categorical variables in mixed IVF dataset.
Annu Int Conf IEEE Eng Med Biol Soc
; 2009: 6214-7, 2009.
Article
em En
| MEDLINE
| ID: mdl-19964898
ABSTRACT
Implantation prediction of in-vitro fertilization (IVF) embryos is critical for the success of the treatment. In this study, Support Vector Machine (SVM) method has been used on an original IVF dataset for classification of embryos according to implantation potentials. The dataset we analyzed includes both categorical and continuous feature values. Transformation of categorical variables into numeric attributes is an important pre-processing stage for SVM affecting the performance of the classification. We have proposed a frequency based encoding technique for transformation of categorical variables. Experimental results revealed that, the proposed technique significantly improved the performance of IVF implantation prediction in terms of Area Under ROC curve (0.712+/-0.032) compared to common binary encoding and expert judgement based transformation methods (0.676+/-0.033 and 0.696 +/- 0.024, respectively).
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Reconhecimento Automatizado de Padrão
/
Inteligência Artificial
/
Fertilização in vitro
/
Técnicas de Apoio para a Decisão
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Avaliação de Resultados em Cuidados de Saúde
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Sistemas de Apoio a Decisões Clínicas
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2009
Tipo de documento:
Article