Selection of human embryos for transfer by Bayesian classifiers

Authors:
Dinora A. Morales;Endika Bengoetxea;Pedro Larrañaga
Affiliations:
Intelligent Systems Group, University of the Basque Country, Donostia-San Sebastián, Spain;Intelligent Systems Group, University of the Basque Country, Donostia-San Sebastián, Spain;Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain
Venue:
Computers in Biology and Medicine
Year:
2008

Citing 13
Cited 4

Signal detection theory: valuable tools for evaluating inductive learning

Proceedings of the sixth international workshop on Machine learning
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
Robust Classification for Imprecise Environments

Machine Learning
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Induction of Decision Trees

Machine Learning
Semi-Naive Bayesian Classifier

EWSL '91 Proceedings of the European Working Session on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Texture Classification of Mouse Liver Cell Nuclei Using Invariant Moments of Consistent Regions

CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS

Journal of Biomedical Informatics - Special issue: Clinical machine learning
The use of receiver operating characteristic curves in biomedical informatics

Journal of Biomedical Informatics - Special issue: Clinical machine learning
Bayesian classification for the selection of in vitro human embryos using morphological and clinical data

Computer Methods and Programs in Biomedicine

Detecting novel hypermethylated genes in Breast cancer benefiting from feature selection

Computers in Biology and Medicine
Texture and moments-based classification of the acrosome integrity of boar spermatozoa images

Computer Methods and Programs in Biomedicine
Learning Bayesian network classifiers from label proportions

Pattern Recognition
Modeling and predicting the occurrence of brain metastasis from lung cancer by Bayesian network: A case study of Taiwan

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work we approach by Bayesian classifiers the selection of human embryos from images. This problem consists of choosing the embryos to be transferred in human-assisted reproduction treatments, which Bayesian classifiers address as a supervised classification problem. Different Bayesian classifiers capable of taking into account diverse dependencies between variables of this problem are tested in order to analyse their performance and validity for building a potential decision support system. The analysis by receiver operating characteristic (ROC) proves that the Bayesian classifiers presented in this paper are an appropriated and robust approach for this aim. From the Bayesian classifiers tested, the tree augmented naive Bayes, k-dependence Bayesian and naive Bayes classifiers showed to perform almost as well as the semi naive Bayes and selective naive Bayes classifiers.