## 3 Materials and Methods
### Data Preprocessing  
Clinical data are generally affected by factors such as missing values, inconsistent data, and exceptions, highlighting the need for preprocessing. Preprocessing is a very important step, as it includes both data preparation and data transformation. [PERSON] [28] recommended processing in different situation to change the distribution of the data and direct the algorithm. Tables 1 and 2 present the descriptive statistics of the dataset attributes, in integer and Boolean form, respectively. To ensure that every attribute contributed equally to the model training of the selected algorithms, the values were normalized in the [0, 1] interval. The calculation of the mean is presented in Equation (A1). Missing information in the dataset can affect the statistical analysis. In the case of the used dataset, several patients refused to provide certain information; therefore, there are many missing values, especially in the case of very intimate questions like those concerning the use of hormonal contraceptives and intrauterine devices, as well as Sexually Transmitted Diseases (STDs). Figure 1: Schematic illustration of the performed research. After analyzing the data, it was evident that several missing values, especially intimate information, belong to the same patients, resulting in the elimination of 105 records, leaving 753 records from the original dataset. The remaining missing values were replaced with the median of the integer data and the most frequently occurring value for the Boolean data. Figure 2 shows a graphical representation of the frequency distributions of several important attributes that were found to be the best predictors in the dataset. The figure also presents the result of the Lilliefors [35, 36] numerical statistical test and estimations of the distribution in the case of the most important variables. Asymmetry to the right is visible, with a sudden decrease in the number of pregnancies per patient, i.e., women with zero, one, or two births. Similarly, for the number of years in which the patient used hormonal contraceptives, there is also an asymmetry to the right, caused by the generally young age of the women in the study. On the other hand, although the patients are relatively young, their age distribution is near symmetrical. The predominance of young age in the dataset may be explained by efforts to a culture of health awareness, where younger women are encouraged to participate in medical examinations and early cancer detection programs. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Attribute** & **Min** & **Max** & **Mean** & **Std Dev** & **Median** & **95\% CI Mean** \\ \hline Age & 13 & 84 & 26.82 & 8.49 & 25 & (26.25, 27.39) \\ Number of sexual partners & 1 & 28 & 2.53 & 1.67 & 2 & (2.41, 2.64) \\ First sexual intercourse & 10 & 32 & 16.99 & 2.80 & 17 & (16.81, 17.18) \\ Number of pregnancies & 0 & 11 & 2.28 & 1.45 & 2 & (2.18, 2.38) \\ Smokes (years) & 0 & 37 & 1.22 & 4.09 & 0 & (0.94, 1.50) \\ Smokes (packs/year) & 0 & 37 & 0.45 & 2.23 & 0 & (0.30, 0.60) \\ Hormonal contraceptives (years) & 0 & 30 & 2.26 & 3.76 & 0.5 & (1.99, 2.53) \\ IUD (years) & 0 & 19 & 0.51 & 1.94 & 0 & (0.37, 0.65) \\ STDs (number) & 0 & 4 & 0.18 & 0.56 & 0 & (0.14, 0.22) \\ STDs: number of diagnoses & 0 & 3 & 0.09 & 0.30 & 0 & (0.07, 0.11) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary statistics of the dataset attributes (integers). Figure 2: Lilliefors normality test results and visual representation for normality validation. The Lilliefors test shows, experimentally, that the data follow a non-normal distribution, and because of this, the median was preferred over the mean [15]. The median is less sensitive to extreme values (outliers) than the mean, but there were no outliers in our dataset. In the case of binary variables, this approach is not suitable. A visual validation of the non-normal distribution is presented in Figure 3 using a Q-Q plot [37].