## 3 Materials and Method
### Data Preprocessing  
To improve the training and testing phase of the dataset and obtain better results, data preprocessing was performed to address irrelevant and redundant data. The data preprocessing stage involved identifying repetitive values, empty data, and outliers in the dataset. Empty data points were detected and filled with the average value of the attribute to which they belonged. Outliers, which are extreme values at the endpoints of the attribute range, were identified. These outlier values were replaced with the average value of the corresponding attribute. In order to ensure that all values have an equal contribution to the performance of the algorithms used in this study, it is important to scale the data to a standardized range. The dataset used in this study was small in size and had a non-normal distribution. Therefore, the mean method was preferred for imputing missing values. This method can make the impact of outliers more pronounced in datasets with a non-normal distribution; however, since the mean is not affected by outliers, the data structure remained intact during the imputation process. In small datasets, the mean method quickly filled in the missing values. This method provided a more accurate representation of the data during model training. The mean method was chosen because it is effective for datasets with a skewed distribution, is not sensitive to outliers, and works efficiently on small datasets. When detecting outliers, factors such as dataset size, structure, computational capacity, and application domain should be considered. If the dataset is small, statistical methods such as the Z-score and IQR are used. These methods are fast and require minimal computation. The structure of the dataset relates to whether the data distribution is normal. If the dataset has a normal distribution, the Z-score method is more effective. If the distribution is non-normal, the IQR method is more suitable. Statistical methods are appropriate for small to medium-sized datasets because they require low computational power. For basic data analyses in specific application domains, statistical methods are usually sufficient. These factors were considered when selecting the method. The IQR method was chosen based on the dataset's small size, non-normal distribution, low computational power, and basic data analysis requirements. The IQR method determines outliers by using the interquartile range of the data. Values greater than or less than 1.5 times the IQR are considered outliers. The presence of outliers in the dataset's attributes was identified, and the outlier data were replaced with the mean of the respective attribute. Figure 2 provides an \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Average** & **Max. Value** & **Min. Value** \\ \hline Gender & 1.44 & 2 & 1 \\ \hline Age & 40.33 & 73 & 1 \\ \hline Size (cm) & 160.67 & 185 & 1 \\ \hline Weight (kg) & 65.75 & 109 & 12 \\ \hline BMI & 30.25 & 36.94 & 9.91 \\ \hline Blood group & 3.64 & 8 & 1 \\ \hline Donor Age & 34.53 & 71 & 0 \\ \hline Donor Gender & 1.34 & 2 & 1 \\ \hline Donor Kinship & 0.87 & 5 & 0 \\ Degree & 168.86 & 190 & 125 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hospital dataset average, minimum, and maximum values. example histogram showing the data distribution of the 'donor weight' attribute from the dataset used in this study. The distribution of the data is visualized in this graph. Outliers are identified as follows: For each attribute, the 0.25 (first quartile) and 0.75 (third quartile) values are calculated, and their difference is used to determine the interquartile range (IQR). Values that fall outside 1.5 times the IQR are considered outliers. After detecting outliers and performing data preprocessing, the distribution of donor weight data changed as shown in Figure 3.  
To achieve this, feature standardization was applied, which scales the data in the dataset from 0 to 1.