# Cervical Cancer Diagnostics Using Machine Learning Algorithms and Class Balancing Techniques
## 6 Results and Discussion
### Shiller  
When the results achieved with the original data set that uses the Shiller variable as an output are compared, it can be seen that the \(\sigma(AUC)\) does not exceed 0.75, regardless of the algorithm used. It can be noticed that the highest results are achieved if SVM, MLP, or Naive Bayes are used, as presented in Figure 13.  
When performances achieved with upsampled data sets are compared, it can be noticed that significantly higher \(\overline{AUC}\) and \(\overline{MCC}\) values are achieved if KNN and MLP models are trained by using random oversampling, SMOTEEN, and SMOTETOMEK. In the case of the listed combinations, \(\overline{AUC}\) values of higher than 0.9 are achieved. The detailed results are presented in Table 11.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Combination** & **Hyper-Parameters** \\ \hline RO-KNN & K = 500 \\ SMOTEEN-KNN & K = 250 \\ SMOTETOMEK-KNN & K = 250 \\ RO-MLP & (100, 100); LBFGS; Logistic Sigmoid \\ \hline \hline \end{tabular}
\end{table}
Table 10: List of hyper-parameters for highest-performing combinations in the case of Hinselmann output.  
Figure 12: Comparison of the highest results achieved with original and balanced data set for the case of Hinselmann output.  
Figure 13: The results achieved with original data set and Shiller output.  
When the results achieved with the highest-performing combinations are compared with the results achieved with the original data set are compared, a significant increase in both classification and generalization performances can be noticed. However, a significant difference between the results achieved with KNN and MLP is noticeable. It can be seen that combinations designed with KNN have significantly higher classification and generalization performances, as presented in Figure 14.  
The hyper-parameters used to construct the above-presented classifiers are presented in Table 12.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{l}{**Class Balancing**} & \(\overline{AUC}\) & \(\sigma(AUC)\) & \(\overline{MCC}\) & \(\sigma(MCC)\) \\ \hline \multirow{4}{*}{Logistic regression} & Random oversampling & 0.63 & 0.14 & 0.33 & 0.12 \\  & SMOTE & 0.68 & 0.09 & 0.32 & 0.08 \\  & ADASYN & 0.61 & 0.07 & 0.21 & 0.08 \\  & SMOTEEN & 0.74 & 0.09 & 0.37 & 0.08 \\  & SMOTETOMEK & 0.60 & 0.14 & 0.15 & 0.07 \\ \hline \multirow{4}{*}{KNN classifier} & Random oversampling & 0.94 & 0.06 & 0.89 & 0.05 \\  & SMOTE & 0.87 & 0.09 & 0.70 & 0.10 \\  & ADASYN & 0.84 & 0.07 & 0.69 & 0.07 \\  & SMOTEN & 0.99 & 0.02 & 0.98 & 0.03 \\  & SMOTETOMEK & 0.98 & 0.02 & 0.92 & 0.02 \\ \hline \multirow{4}{*}{SVM classifier} & Random oversampling & 0.69 & 0.17 & 0.38 & 0.17 \\  & SMOTE & 0.76 & 0.20 & 0.58 & 0.19 \\  & ADASYN & 0.62 & 0.08 & 0.24 & 0.08 \\  & SMOTEEN & 0.81 & 0.15 & 0.47 & 0.13 \\  & SMOTETOMEK & 0.79 & 0.13 & 0.38 & 0.14 \\ \hline \multirow{4}{*}{MLP Classifier} & Random oversampling & 0.92 & 0.06 & 0.91 & 0.06 \\  & SMOTE & 0.89 & 0.05 & 0.88 & 0.05 \\  & ADASYN & 0.86 & 0.04 & 0.80 & 0.06 \\  & SMOTEEN & 0.94 & 0.04 & 0.92 & 0.04 \\  & SMOTETOMEK & 0.90 & 0.05 & 0.87 & 0.04 \\ \hline \multirow{4}{*}{Naive Bayes classifier} & Random oversampling & 0.68 & 0.01 & 0.65 & 0.01 \\  & SMOTE & 0.75 & 0.02 & 0.72 & 0.02 \\ \cline{1-1}  & ADASYN & 0.75 & 0.01 & 0.72 & 0.01 \\ \cline{1-1}  & SMOTEEN & 0.71 & 0.01 & 0.62 & 0.01 \\ \cline{1-1}  & SMOTETOMEK & 0.74 & 0.01 & 0.56 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results achieved with Shiller output.  
Figure 14: Comparison of the highest results achieved with original and balanced data set for the case of Shiller output.