# Cervical Cancer Diagnostics Using Machine Learning Algorithms and Class Balancing Techniques
## 6 Results and Discussion
### Hinselmann  
As it is in the case of biopsy and cytology, if the original data set with Hinselmann as the output variable is used, low classification and generalization performances are achieved. In this case, the \(\sigma(AUC)\) value does not exceed 0.75, as presented in Figure 11.  
When the class oversampling techniques are applied, it can be noticed that \(\overline{AUC}\) and \(\overline{MCC}\) values of higher than 0.9 are achieved if KNN and MLP are used. In the case of KNN, three different combinations are achieving higher performances than MLP combined with random oversampling. The detailed results are presented in Table 9. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Classifier** &
\begin{tabular}{l} **Class Balancing** \\ **Technique** \\ \end{tabular} & \(\overline{AUC}\) & \(\sigma(AUC)\) & \(\overline{MCC}\) & \(\sigma(MCC)\) \\ \hline \multirow{6}{*}{Logistic regression} & Random oversampling & 0.60 & 0.03 & 0.20 & 0.03 \\  & SMOTE & 0.73 & 0.02 & 0.46 & 0.02 \\  & ADASYN & 0.74 & 0.02 & 0.46 & 0.02 \\  & SMOTEEN & 0.75 & 0.03 & 0.49 & 0.03 \\  & SMOTETOMEK & 0.73 & 0.02 & 0.36 & 0.03 \\ \hline \multirow{6}{*}{KNN classifier} & Random oversampling & 0.95 & 0.04 & 0.91 & 0.04 \\  & SMOTE & 0.89 & 0.09 & 0.77 & 0.08 \\  & ADASYN & 0.84 & 0.12 & 0.66 & 0.11 \\  & SMOTEEN & 0.92 & 0.05 & 0.84 & 0.05 \\  & SMOTETOMEK & 0.95 & 0.04 & 0.88 & 0.04 \\ \hline \multirow{6}{*}{SVM classifier} & Random oversampling & 0.74 & 0.10 & 0.49 & 0.12 \\  & SMOTE & 0.69 & 0.14 & 0.64 & 0.13 \\  & ADASYN & 0.73 & 0.09 & 0.46 & 0.09 \\  & SMOTEN & 0.73 & 0.19 & 0.38 & 0.24 \\  & SMOTETOMEK & 0.74 & 0.12 & 0.33 & 0.12 \\ \hline \multirow{6}{*}{MLP Classifier} & Random oversampling & 0.96 & 0.08 & 0.95 & 0.08 \\  & SMOTE & 0.87 & 0.04 & 0.87 & 0.04 \\  & ADASYN & 0.88 & 0.07 & 0.88 & 0.04 \\  & SMOTEEN & 0.91 & 0.03 & 0.88 & 0.04 \\  & SMOTETOMEK & 0.92 & 0.03 & 0.89 & 0.03 \\ \hline \multirow{6}{*}{Naive Bayes classifier} & Random oversampling & 0.71 & 0.21 & 0.52 & 0.19 \\  & SMOTE & 0.69 & 0.03 & 0.62 & 0.03 \\ \cline{1-1}  & ADASYN & 0.70 & 0.03 & 0.70 & 0.03 \\ \cline{1-1}  & SMOTEEN & 0.73 & 0.12 & 0.59 & 0.14 \\ \cline{1-1}  & SMOTETOMEK & 0.77 & 0.10 & 0.69 & 0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results achieved with cytology output. \begin{table}
\begin{tabular}{l l} \hline \hline
**Combination** & **Hyper-Parameters** \\ \hline RO-KNN & K = 100 \\ SMOTEEN-KNN & K = 100 \\ RO-MLP & (100, 100); LBFGS; Tanh \\ SMOTEEN-MLP & (100, 10); LBFGS; ReLU \\ \hline \hline \end{tabular}
\end{table} 
Table 8: List of hyper-parameters for highest-performing combinations in the case of cytology output. If the results presented in Table 9 are compared with the results achieved on the original data set, it can be seen the \(\sigma(AUC)\) values achieved with upsampled data sets are significantly higher, regardless of the classifier and class balancing techniques used. However, in the case of MLP trained with a randomly oversampled data set, \(\sigma(AUC)\) is significantly higher, as presented in Figure 12. For these reasons, it can be concluded that KNN-based algorithms are characterized by higher generalization performances. Figure 11: The results achieved with original data set and Hinselmann output.