# Diagnosis and Prediction of Large-For-Gestational-Age Fetus Using the Stacked Generalization Method
## 5 Discussions and Comparative Analysis with Existing State-of-the-Art LGA Classifications
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Experiment Type** & **Best Classifier** & **Size** & **Precision** & **AUC** & **Recall** & **Accuracy** & **Specificity** & **F1** \\ \hline \begin{tabular}{c} GirdSearch with \\ tuned parameters \\ \end{tabular} & \begin{tabular}{c} SVM \\ (linear kernel) \\ \end{tabular} & All & 0.949 & 0.843 & 0.646 & 0.842 & 0.976 & 0.766 \\ \hline \begin{tabular}{c} GirdSearch + RFECV \\ + Information Gain \\ \end{tabular} & \begin{tabular}{c} SVM \\ (linear kernel) \\ \end{tabular} & 10 & 0.971 & 0.868 & 0.606 & 0.833 & 0.987 & 0.744 \\ \hline \
\begin{tabular}{c} GridSearch + RFECV + IG \\ + Stack generalization \\ \end{tabular} &
\begin{tabular}{c} Stacked SVM \\ (linear kernel) \\ \end{tabular} & 10 & 0.920 & 0.950 & 0.8683 & 0.9156 & 0.9478 & 0.8921 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative best results of all three group of experiments with the proposed ensemble of feature selection and extraction techniques with stack-generalization. Moreover, Friedman and Bonferroni-Dunn tests are also introduced to rank and highlight the significance difference between the results reported in Figures 4 and 5. Initially, Friedman test considering \((p<0.05)\) is employed to rank the classifiers based on the result of said experiments. The longitudinal axis in Figures 6 and 7 represents the average mean ranking calculated by using Friedman test on all group of experiments. From the results it is foreseen that SVM with linear kernel outperformed in almost each group of experiment. In addition, Bonferroni-Dunn test is also employed in the significance level of \(\alpha<0.05\), \(\alpha<0.01\), and \(\alpha<0.001\) to the results of Friedman test. Equation (10) is used to calculate Critical Distance (CD) used in Bonferroni-Dunn Test. Based on provided guidelines by the author of [45], for Figure 6, we selected \(N=6\), and \(k=4\), \(q=\) is equal to \(q_{a}(0.05)=3.4077\), \(q_{a}(0.01)=4.089\), and \(q_{a}(0.001)=4.9198\) whereas, for Figure 7, \(N=5\), and \(k=4\), \(q=\) is equal to \(q_{a}(0.05)=3.3045\), \(q_{a}(0.01)=4.004\), and \(q_{a}(0.001)=4.8444\). Based on these figures' results, it is observed that SVM has the largest difference in-between pair-wise means of control group with the critical values which validates the previously concluded remarks of using ranked ten features subset with SVM (linear kernel) classifier as an important mean to diagnose infants as LGA or non-LGA. Furthermore, the proposed scheme has the potential to classify various disease classes accurately using gestational parameters as suggested by the panel of experts of different domains. The limitation of the proposed scheme is that it experiments only for LGA dataset. However, it has the potential to produce accurate results for Small for Gestational Age (SGA) infants as well, which we will explore in our future work. In addition, as previously discussed that machine learning techniques on LGA have never been exercised extensively, so this research presents an extensive work that can facilitate paediatricians and researcher to extend their research in the defined area. Moreover, in our future work, deep learning techniques, such Standard Deep Neural network (NN) [46], Hierarchical deep learning (HDL) [47], Random multimodel deep learning (RMDL) or deep perceptron [48] will also be exploited to add more scientific results to the related domain.