A frequency based encoding technique for transformation of categorical variables in mixed IVF dataset.
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).
Uyar
Asli
A
Bogazici University, 34342 Bebek Istanbul, Turkey.
Bener
Ayse
A
Ciray
H
H
Bahceci
Mustafa
M
D000465:Algorithms; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D005307:Fertilization in Vitro; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011379:Prognosis