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 plusmn 0.032) compared to common binary encoding and expert judgement based transformation methods (0.676 plusmn 0.033 and 0.696 plusmn 0.024, respectively).