## 5 Discussion  
The performance comparison of the proposed work with the state-of-the-art works in coronary heart disease risk prediction is provided in Table 16. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**No.** & **Ref.** & **Year** & **FS** & **Classifier** & **\#** & **Acc.** & **Sen.** & **Spe.** & **Pre.** & **F-mea.** & **AUC** \\ \hline
1. & [31] & 2019 & RSA & RF & 7 & 93.33 & 95.12 & 89.79 & - & - & 0.947 \\ \hline
2. & [32] & 2019 & Relief & RF & 12 & 85.50 & 85.60 & - & 85.50 & 85.50 & 0.915 \\ \hline
3. & [33] & 2020 & PPA & KNN & 4 & 86.17 & - & 85.00 & - & - & - \\ \hline
4. & [34] & 2020 & NFR & LR & 9 & 92.53 & - & - & - & - & 0.926 \\ \hline
5. & [35] & 2019 & FFSA & SVM RBF & 7 & 81.19 & 72.92 & 88.68 & - & - & - \\ \hline
6. & [36] & 2021 & LASSO & DL & 6 & 94.20 & 83.10 & 82.30 & - & - & - \\ \hline
7. & [37] & 2020 & Relief & Extra Tree & 6 & 94.41 & 94.93 & 94.89 & 95.46 & 95.00 & 0.942 \\ \hline
8. & [38] & 2019 & Chi-square & DL & 11 & 93.33 & 85.36 & 100 & - & - & 0.94 \\ \hline
9. & [39] & 2022 &
\begin{tabular}{c} Pearson’s \\ correlation \\ \end{tabular} & CatBoost & 10 & 94.34 & - & - & - & - & - \\ \hline
10. & [40] & 2022 & - & LR & 13 & 92.30 & 96.08 & 87.50 & 90.74 & 93.34 & - \\ \hline
11. & [41] & 2022 & - & SVM & 13 & 96.72 & 98.00 & - & 98.00 & 98.00 & - \\ \hline
12. & [42] & 2022 & Correlation & RF & - & 83.00 & - & - & - & - & - \\ \hline
13. & [45] & 2020 & RTs & RTs & 44 & 91.47 & - & - & - & - & 0.967 \\ \hline
14. & [46] & 2020 & Tree-based &
\begin{tabular}{c} BiGRU \\ +CNN \\ \end{tabular} & 60 & 96.00 & 92.00 & - & 99.00 & 95.00 & 0.990 \\ \hline
15. & \
\begin{tabular}{c} Proposed \\ work \\ \end{tabular} & 2022 & PCA + CFS &
\begin{tabular}{c} Rotation \\ Forest \\ \end{tabular} & 6 & 97.91 & 97.91 & 97.66 & 97.92 & 97.91 & 0.996 \\ \hline \end{tabular}
\end{table} 
Table 16: Performance comparison of related works. Figure 11: Cost comparison of machine learning models without and with optimization. From Table 16, although prior studies employed feature selection and ensemble techniques, the highest accuracy obtained was 94.41% by [PERSON] et al. [37] with the use of 6 optimal features (age, cp, fbs, ca, thal, and thalach) of the Cleveland dataset obtained from the Relief feature selection method and Extra Tree classifier. [PERSON] et al. [36] attained 94.20% with the six features (age, sex, exang, oldpeak, ca, and trestbps) obtained from the LASSO FS technique and Deep Learning. [PERSON] et al. [38] achieved 100% specificity on the test set of 90 samples, but the accuracy (93.33%) is about 7% less, and the sensitivity (85.36%) is about 15% less than the specificity achieved. [PERSON] et al. [31] and [PERSON] et al. [37] attained an AUC value of 0.947 and 0.942, respectively. [PERSON] et al. [41] achieved the utmost accuracy of 96.72% by standardization and tuning the hyperparameters of the SVM model on the full features of the Cleveland dataset. [PERSON] et al. [45] used the random trees to rank the features of [PERSON]'s heart data and achieved 91.47% of accuracy with the 44 selected features. The ensemble of deep learning networks employed in [46] selected 60 features from a tree-based random classifier and achieved 99% of precision but the accuracy is 3% less than that. In this work, eighteen (18) principal components have been obtained by specifying parameters variance covered as 0.95 and maximum attributes names as 5. Then, CFS with the best first search method was applied to select the best components to reduce the training time and we utilized 10-fold cross-validation for evaluating the performance of the machine learning classifiers. Cross-validation is a statistical resampling approach that tests and trains a machine learning model using various parts of the data on successive iterations. It also mitigates the risk of overfitting. Further, hyperparameter tuning of classifiers has been carried out to improve the predictive performance of the classifiers.