## 4 Results and Discussion
### Association Rule Mining  
Features from PCA, RFE and graph-based selection methods were discretized and analyzed to find possible associations using Apriori. The resulting number of rules, maximum confidence, support and lift values are summarized in Table 4.  
The graph-based feature-selection approach gave 15 and 36 non-redundant rules, respectively, from the two datasets at a support of 0.5 confidence value of 0.9 and a lift of 2. The other feature-selection methods did not generate any rules at a support of 0.5. Features selected by RFE had the lowest maximum support and lift, and this led to the generation of too many redundant rules. For the PCA-based feature selection, support ranged between 0.405 and 0.425 with a total of 38 rules for the first dataset and 36 rules for the second dataset (Table 4). The top 10 rules are shown in Table 5. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**NAIVE BAYES**} \\ \hline
**Dataset** & **Feature Selection Method** & **Accuracy** & **MAE** & **Kappa** & **RMSE** & **F-Measure** & **T/s** \\ \hline \multirow{3}{*}{**GSE60052**} & Graph-based & 96.4286 & 0.0357 & 0.8679 & 0.189 & 0.963 & 0.01 \\  & RFE & 100 & 0 & 1 & 0 & 1 & 0.01 \\  & PCA & 96.4286 & 0.0357 & 0.8679 & 0.189 & 0.963 & 0.02 \\ \hline \multirow{3}{*}{**GSE81089**} & Graph-based & 100 & 0 & 1 & 0 & 1 & 0.06 \\  & RFE & 100 & 0 & 1 & 0 & 1 & 0.01 \\  & PCA & 100 & 0 & 1 & 0 & 1 & 0.02 \\ \hline \multicolumn{10}{c}{**MULTILAYER PERCEPTRON**} \\ \hline
**Dataset** & **Feature Selection Method** & **Accuracy** & **MAE** & **Kappa** & **RMSE** & **F-Measure** & **T/s** \\ \hline \multirow{3}{*}{**GSE60052**} & Graph-based & 96.4286 & 0.0366 & 0.8679 & 0.1814 & 0.979 & 18.66 \\  & RFE & 96.4286 & 0.0389 & 0.8679 & 0.1851 & 0.963 & 9.7 \\  & PCA & 96.4286 & 0.0224 & 0.8679 & 0.0993 & 0.963 & 9.62 \\ \hline \multirow{3}{*}{**GSE81089**} & Graph-based & 96.4286 & 0.0389 & 0.8679 & 0.1851 & 0.963 & 124.15 \\  & RFE & 100 & 0 & 1 & 0 & 1 & 131.53 \\  & PCA & 100 & 0 & 1 & 0 & 1 & 0.77 \\ \hline \multicolumn{10}{c}{**SEQUENTIAL MINIMAL OPTIMIZATION**} \\ \hline
**Dataset** & **Feature Selection Method** & **Accuracy** & **MAE** & **Kappa** & **RMSE** & **F-Measure** & **T/s** \\ \hline \multirow{3}{*}{**GSE60052**} & Graph-based & 96.4286 & 0.0357 & 0.8679 & 0.189 & 0.889 & 0.01 \\  & RFE & 96.4286 & 0.0357 & 0.8679 & 0.189 & 0.963 & 0.01 \\  & PCA & 100 & 0 & 1 & 0 & 1 & 0.02 \\ \hline \multirow{3}{*}{**GSE81089**} & Graph-based & 98.5915 & 0.0141 & 0.9567 & 0.1187 & 0.986 & 0.14 \\  & RFE & 100 & 0 & 1 & 0 & 1 & 0.01 \\ \cline{1-1}  & PCA & 100 & 0 & 1 & 0 & 1 & 0.01 \\ \hline \end{tabular}
\end{table} 
Table 3: Classification results after feature selection. Association rules are represented as \(X\Rightarrow Y\), where X and Y are items contained within a dataset/database, and \(X\cap Y=\emptyset\). \(X\) is the antecedent, and \(Y\) is the consequent (Table 5). It means that whenever X, which is the antecedent, is present, even Y, which is the consequent, will be present. Support indicates the frequency of the itemset appearance in the dataset, and the confidence indicates how often a rule has been found to be true. A support value of 0.5 means 50% of the items (genes) are found in the transaction and 90% of the rules are true (Confidence). The lower support means that most of the items are not frequently found together. The lift value is used to measure the rule importance. A lift of greater than 2 achieved by the graph-based feature-selection approach indicates the degree to which any two occurrences depend on each other, and this is an indication those rules are useful in consequent prediction. External factors that play a critical role in the success of this type of study include the choice of the technology used in generating the data since it determines the volume and quality of the data across the replicates. The cost of generating the data also limits the number of samples as well as the volume of data.