Research Article
ROC Based Evaluation and Comparison of Classifiers for IVF Implantation Prediction
@INPROCEEDINGS{10.1007/978-3-642-11745-9_17, author={Asli Uyar and Ayse Bener and H. Ciray and Mustafa Bahceci}, title={ROC Based Evaluation and Comparison of Classifiers for IVF Implantation Prediction}, proceedings={Electronic Healthcare. Second International ICST Conference, eHealth 2009, Istanbul, Turkey, September 23-15, 2009, Revised Selected Papers}, proceedings_a={E-HEALTH}, year={2012}, month={5}, keywords={}, doi={10.1007/978-3-642-11745-9_17} }- Asli Uyar
Ayse Bener
H. Ciray
Mustafa Bahceci
Year: 2012
ROC Based Evaluation and Comparison of Classifiers for IVF Implantation Prediction
E-HEALTH
Springer
DOI: 10.1007/978-3-642-11745-9_17
Abstract
Determination of the best performing classification method for a specific application domain is important for the applicability of machine learning systems. We have compared six classifiers for predicting implantation potentials of IVF embryos. We have constructed an embryo based dataset which represents an imbalanced distribution of positive and negative samples as in most of the medical datasets. Since it is shown that accuracy is not an appropriate measure for imbalanced class distributions, ROC analysis have been used for performance evaluation. Our experimental results reveal that Naive Bayes and Radial Basis Function methods produced significantly better performance with (0.739 ± 0.036) and (0.712 ± 0.036) area under the curve measures respectively.