## 7 Conclusions  
Machine learning algorithms (MLAs) are important in healthcare because they analyze medical data to detect human cardiac disease. CVDs are a serious medical condition that healthcare providers and researchers must address. In this paper, twelve strategies are considered for doing comparative analysis and obtaining positive findings. In this investigation, it is concluded that machine learning algorithms beat human learning algorithms. Some of the comparison approaches used are the confusion matrix, precision, recall, F1-score, and ROC-AUC. For the 14 characteristics dataset, the random forest classifier performed better in the ML approach when data pre-processing was utilized. To address this issue, MLAs are employed to assess a dataset of CVD clinical data to identify the critical risk factors that influence CVD progression. To improve the training and testing of the algorithms, the random forest and decision tree are used. Then, using the train-test split approach, the prediction models' classification performance on combined datasets is examined. Finally, comparisons of the findings are given. Accuracy, precision, recall, F1-score, and ROC-AUC are used as performance indicators, for the Cleveland heart disease dataset, whereas Switzerland, Hungarian, and Long Beach VA datasets were used to predict CVD. The following are the results from the combined heart disease dataset: F1-score is 100%, accuracy is 100%, precision is 100%, and recall is 100%. The following are the analytical results of the decision tree classifier for the pooled heart disease dataset: F1-score of 100%, accuracy of 98.80%, precision of 98%, recall of 99%, ROC-AUC of 99%, and the performance of ROC-AUC of 100% for top models' random forest and gradient boosting classifier, respectively. The performances of the machine learning algorithms are improved by using five-fold cross validation. Again, the Stacking CV Classifier is also used to improve the performances of the individual machine learning algorithms by combining two and three techniques together. Several reduction methods may also be used to improve the random forest classification algorithm's accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Methods** & **Accuracy** & **Recall** & **Precision** & **F1-Measure** & **ROC-AUC** \\ \hline RF & 100 & 100 & 100 & 100 & - \\ \hline DT & 100 & 100 & 100 & 100 & - \\ \hline GB & 89.26 & 89 & 89 & 89 & - \\ \hline KNN & 86.34 & 86.19 & 86.25 & 86.04 & - \\ \hline AB & 90.00 & 90.00 & 90.00 & 90.00 & - \\ \hline LR & 86 & 87 & 86 & 86 & - \\ \hline NN & 97 & 97 & 97 & 97 & - \\ \hline QDA & 88 & 88 & 88 & 88 & - \\ \hline Nu SVC & 93.17 & 93 & 93 & 93 & - \\ \hline LDA & 86 & 87 & 86 & 86 & - \\ \hline NB & 87 & 87 & 87 & 87 & - \\ \hline SVM & 99 & 98 & 99 & 99 & - \\ \hline RFC+NN+SVM & 100 & 100 & 100 & 100 & - \\ \hline RFC+NN+QDA & 100 & 100 & 100 & 100 & - \\ \hline RFC+NN & 100 & 100 & 100 & 100 & - \\ \hline LDA+QDA & 89 & 89 & 89 & 89 & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._  
**Author Contributions:** Conceptualization, G.N.A. and S.; methodology, G.N.A. and S.; software, G.N.A.; validation, H.F. and G.N.A.; formal analysis, G.N.A., S. and H.F.; investigation, G.N.A.; resources, I.; writing--original draft preparation, G.N.A. and S.; writing--review and editing. I. and M.A.; visualization, O.R. and M.S.A.; supervision, H.F. and S.; project administration, M.A. and M.S.A.; funding acquisition, M.A., O.R. and M.S.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University (KKU) for funding this research project Number (RGP2/248/43).  
**Conflicts of Interest:** The authors declare no conflict of interest.