## 4 Discussion
It can be noticed from Table 7 that the recommended model, EXP2-LR, had the best accuracy (99.56%), F-measure (73.24%), and MCC (74.30%) results. Table 8 compares the results achieved in this paper for the EXP2-LR combination with those of the previous studies in terms of \(\beta\) and \(X_{c}\) values, which indicate the effectiveness of the selected classifiers for binary classification. It can be noticed from the table that, the previously reported methods all provided unbiased outcomes (\(\beta\) values were close to zero) and conservative decision-making (\(X_{c}\) is negative). While the \(\beta\) values of the previous methods indicate perfect unbiased results, the related \(\beta_{opt}\) values were high, which means that the validated results were not sufficiently generalized. In other words, the previous studies pay more attention to examples with the NEG class label than those with the POS class label. They may be able to minimize the FN score but at the cost of increasing FP scores. Furthermore, the \(X_{c}\) values of all previous methods were smaller (i.e., bigger negative numbers) than that of the EXP2-LR model, which indicates that previous methods provided more conservative decisions. These results demonstrated that the EXP2-LR model recommended in this paper achieved the best performance in the considered binary classification task. Two further findings were discussed here. First, according to the SDT, when the \(\beta\) values were close to zero, the selected model was implemented with few biases. This characteristic is adjusted by applying additional weights such as penalties and rewards (usually are predefined by experienced experts). Previous studies published between 2016 and 2019 March implemented various imputation techniques; however, these manipulations do not improve the recognition ability of the reported models. In contrast, adding weight values might cause more conservative decision-making. The second finding is about data representativeness after resampling. An inappropriate resampling method can lead to new problems, e.g., insufficient fitting, even if it improves the original class imbalance. [PERSON] et al. [15] compared actual data to under-sampled data. As shown in Table 8, the \(\beta\) value calculated for the under-sampled data was more unbiased (\(\beta\) was 0.0000, i.e., perfectly unbiased) \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Source** & **MD Imputation** & **Classifier** & **Accuracy** & **F-Measure** & **MCC** \\ \hline [PERSON] and [PERSON] (2016) & Mean, Soft Impute & RF & 96.56\% & 57.05\% & 61.55\% \\ [PERSON], [PERSON], and [PERSON] (2016) & Median & RF & 96.86\% & 59.12\% & 63.08\% \\ [PERSON] et al. (2016) & N/A & N/A & 97.42\% & 63.55\% & 66.55\% \\ [PERSON] and [PERSON] (2017) & Mean & RF & 96.53\% & 56.67\% & 61.09\% \\ [PERSON] et al. & MICE & RF & 94.79\% & 46.81\% & 53.32\% \\ (2019)—Under-sampling & & & & & \\ [PERSON] et al. (2019)—Actual Data & Interactive SVD & NB & 96.39\% & 53.76\% & 57.32\% \\ [PERSON] and [PERSON] (2019) & k-NN (k = 33) & Modified RF & 97.29\% & 62.46\% & 65.70\% \\ EXP2-LR (this paper) & No Impute & LR & 99.56\% & 73.24\% & 74.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the proposed approach to methods reported in previous studies based on accuracy, F-measure, and Matthews correlation coefficient (MCC) over the test set. than that calculated for the actual data (\(\beta\) was 0.0013, i.e., very unbiased). However, the \(\beta_{opt}\) and \(X_{c}\) values calculated for the under-sampled data (0.0550 and \(-\)2.8358, respectively) were farther from zero than those for the actual data (0.0375 and \(-\)2.1683, respectively), which means the under-sampled data were more conservative. Regardless of the resampling method used (under- or over-sampling), the recommendation for the subsequent processing on the transformed data was to first check the consistent distribution assumption.