## 4 Result and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Missing-Value** & **Classification Algorithm** & **Methods** & **Selected Features** \\ \hline \multirow{4}{*}{Mean and mode} & \multirow{4}{*}{Logistic Regression} & Lasso Regression & 2, 4, 8, 7, 11, 15, 14, 17,16, 21, 20, 23, 22, 24, 27, \\  & & 30, 29, 31, 34, 36, 39, 42, 43, 41, 45, 47,46, 49 \\ \cline{2-4}  & & Ridge Regression & 7, 10, 11, 16, 23, 24, 26, 29, 30, 32, 38, 39, 41, 43, 46, 47, 49 \\ \cline{2-4}  & \multirow{4}{*}{Recursive Feature Elimination} & Gradient Boosting & 26, 31, 41 \\ \cline{2-4}  & & Random Forest & 27, 29, 30,32,31, 34, 36, 35,37, 40, 39, 41,46, 42, 47,48,49 \\ \hline \multirow{4}{*}{k-NN} & \multirow{4}{*}{Logistic Regression} & Lasso Regression & 2, 4, 7, 8, 10,11,12,13, 14, 15, 18, 21, 24, 26, 28, \\  & & 29, 31, 34, 36, 39, 41, 42, 43, 46 \\ \cline{1-1} \cline{2-4}  & & Ridge Regression & 2, 7, 10, 11, 16, 23, 24, 26, 29, 30, 35, 38, 39, 41, 43, 44, 47, 49 \\ \cline{1-1} \cline{2-4}  & & Gradient Boosting & 2, 24, 31, 32, 39, 47,49 \\ \cline{1-1} \cline{2-4}  & & Random Forest & 24, 27, 31, 32, 40, 45, 41, 48,49 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Features selected based on different feature selections. Figure 5: k-NN method without using feature selection. Figure 8: Ridge regressions for mean and mode. Figure 6: LASSO regressions for mean and mode. Figure 7: LASSO regression for k-NN. Table 2 shows the l2- regularization-based feature selection employing k-NN to handle missing values. This approach identifies eighteen features from the dataset. Classification using the selected features is performed with the different machine learning algorithms. Figure 9 indicates that RF produces a greater accuracy value and a higher F1-score value, both of 90%, when employing the parameter values of n estimators = 200, criterion = \"entropy\", random_state = 60. Features selection, based on using the GB classifier as an estimator, is shown in Table 2. Here, the total numbers of features selected are three. The experimental result showed that Random Forest gives a greater accuracy value and a higher F1-score value, of 95.12% and 93.75%, respectively, using the parameter values of n_estimators = 100 and criterion = 'gini', as shown in Figure 10. Features selected based on the GB classifier as an estimator with k-NN are shown in Table 2. Seven features are selected. The experimental result showed that the decision tree with parameter values of max_depth = 9 and random_state = 100 give the greatest accuracy value and highest F1-score value, of 96.67% and 96.55%, respectively, as shown in Figure 11.  
Figure 10: Gradient boosting method for mean and mode. Figure 5:0 Ridge regressions for k-NN. Features selected based on the Random Forest classifier as an estimator, with mean and mode as the missing-data-handling method, are shown in Figure 5:1 Seventeen features are selected. Features selected based on the Random Forest classifier as an estimator are shown in Figure 5:2 Nine features are chosen using this method. It is noticed that the RF provides a greater accuracy value and a higher F1-score value, of 95.12% and 94.11%, respectively, when using the parameters of n_estimators = 100 and criterion = 'gini', as depicted in Figure 5:3 Similarly, the experimental result exhibits that the RF produced a 95.01% accuracy value and a 94.54% F1-score value, while using the default values of the parameters as n_estimators = 100 and criterion = 'gini', as shown in Figure 5:4  
Comparing all feature-selection and classifier methods, the recursive feature-selection method using the GB classifier as an estimator is the best technique to increase the predictive model's performance for a smaller number of selected features, with accuracy and F1-score values of 96.66% and 96.55%. RFE-GB gives fewer selected features with better accuracy with the essential elements, such as symptoms, age, alpha-fetoproteins, hemoglobin, aspartate-transaminase, iron, and ferritin, when using the k-NN method for missing-value replacement.