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).

体外受精（IVF）胚胎的着床预测对于治疗的成功至关重要。在这项研究中，支持向量机（SVM）方法已应用于原始的体外受精数据集，以便根据着床潜能对胚胎进行分类。我们分析的数据集包含分类特征值和连续特征值。将分类变量转换为数值属性是影响分类性能的支持向量机的一个重要预处理阶段。我们提出了一种基于频率的编码技术用于分类变量的转换。实验结果表明，与常见的二进制编码和基于专家判断的转换方法（分别为0.676±0.033和0.696±0.024）相比，所提出的技术在受试者工作特征曲线下面积（0.712±0.032）方面显著提高了体外受精着床预测的性能。