Preprocessing of data is one of the most essential steps in the knowledge discovery tasks. A previous study have stated that 80% of total time in data mining projects is allocated for data preparation and preprocessing step [23].

In the first step, the initial collected dataset includes almost 86,000 data records describing the partners and about 1,000 features. The data records describing one couple per IUI treatment cycle are aggregated to make our dataset. Thus, the aggregated dataset includes 11,255 data records and 296 features describing a couple during an IUI treatment cycle.

The nominal features are converted to dummy binary variables. If a nominal features has m different levels or values, it will be converted to (m-1) dummy binary variables. Therefore, instead of considering a nominal feature in the classification and feature ranking, its corresponding dummy binary variables are considered in the mentioned tasks.

The missing values for numeric and categorical features are imputed based on the average and the most frequent values, respectively [24]. All numerical and ordinal features are normalized using min–max normalization method and the nominal features are converted into dummy binary variables.

Outlier detection is performed in this study based on isolation forest method which has been proposed by Liu et al. [25] as an appropriate outlier detection method for high dimensional data. The hyperparameters of Isolation Forest including the number of estimators, maximum number of the samples, contamination coefficient, maximum number of the features, bootstrapping or not, and the number of jobs are tuned using grid search method. For evaluating the performance of Isolation Forest, its results are compared to other outlier detection methods such as One-class SVM with kernel of Radial Basis Function (RBF), boxplot analysis and expert's opinions. Three outliers are identified by this method and excluded from the study.

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