# 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  
The proposed scheme for the classification of LGA fetus using stacked generalization with an ensemble feature selection scheme proved best in the selection of useful features subset which can accurately identify a fetus with its gestational parameters. From the results, it is also evident that the ranked ten principal features subset by every feature selection scheme remained best among all feature subsets and produced its highest prediction performance metrics scores. Table 4 presents the comparative best results of all three group of experiments with the proposed ensemble of feature  
Figure 5: Comparative results of various stacked-classifiers on different features subset obtained as the result of applying GridSearch (for parameters tuning), RFECV feature selection scheme, Information Gain (IG) feature selection scheme for ranking of features, and Stacking to extract new features and to eliminate classifiers generalization errors. selection and extraction techniques with stack-generalization. From the results, it is observed that among the three experimentations, SVM (linear kernel) classifiers outperformed with the production of highest prediction performance metrics scores and remained best with prediction precision, recall, accuracy, AUC, specificity, and F1 scores of 0.92, 0.87, 0.92, 0.95, 0.95, and 0.89 respectively. The reason for being the best is because of the formation of maximum hyperplane between the LGA and non-LGA class. The creation of maximum hyperplane is possible because of easily superable feature subsets induced as the result of applying the proposed ensemble of feature selection and extraction schemes with stacked generalization. SVM (RBF kernel) classifier is also suitable for LGA classification task because of its impressive results but is not recommended because of its computational complexity. Furthermore, LR classifier can also be used for the said classification task, but DT classifier is never recommended due to its low performance. The reason for DT classifier for being insignificant might be because of inadequacy in applying regression and possibility of duplication with the same sub-tree on different paths while predicting values. The significance of the proposed scheme is highlighted by comparing the results of the proposed scheme with existing state-of-arts LGA classification schemes. Table 5 presents the comparative best results of recently published schemes on the same dataset with the proposed scheme. The results reveals that the highest prediction performance metrics scores (i.e., precision = 0.92, AUC = 0.95, recall = 0.87, accuracy = 0.92, specificity = 0.95, and, F1 = 0.89) are obtained by the proposed scheme with SVM (linear kernel) using ten principal features subset. Table 6 present the results of ranked ten principal feature subset of GridSearch-based RFECV + IG feature selection scheme with four ML classifiers using ten-fold cross-validation. From the comparative analysis of the results of the proposed scheme, it is also discerned that the feature engineering and classification schemes of this research best suits the process of establishing a state-of-art LGA prognosis process with improved classification performance with lesser computational overhead. The reason for the improvement in classification performance is because of the extraction of reduced numbers of discriminant features subset, which eventually helps in removing LGA classifiers complexity with decreased generalization errors to improve LGA classification accuracy. \[CD=q_{\alpha}\sqrt{\frac{k(k+1)}{6*N}} \tag{10}\]