## Appendix B  
This Appendix provides a comparison between different algorithms with different datasets that are used for cardiovascular disease prediction. Noting that the approaches in the table are given as follows: Classification and regression tree algorithm (CART), heart disease dataset (IEEE Dataport), Machine Learning based Cardiovascular Disease Diagnosis (MaLCaDD), hybrid random forest with a linear model (HRFLM), accuracy (ACC), precision (Pr), recall (Re), and F1-score (F1).  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Paper** & **Year** & **Dataset Used** & **Algorithms Used** & **ACC\%** & **Pr\%** & **Re\%** & **F1\%** \\ \hline
[147] & 2023 & Comprehensive & Quine McCluskey Binary Classifier & & & & \\ UCI datasets & & (QMBC) (LR, DT, RF, KNN, NB, SVM, and MLP) & 98.31\% & 96.89\% & 100\% & 98.42\% \\ \hline
[147] & 2023 & Cardiovascular & Quine McCluskey Binary Classifier & & & & \\ Disease dataset & (QMBC) (LR, DT, RF, KNN, NB, SVM, and MLP) & 99.95\% & 100\% & 99.91\% & 99.95\% \\ \hline
[231] & 2023 & Cleveland & Deep ANN, LSTM, CNN, and hybrid CNN with LSTM & 97.75\% & 98.57\% & 97.87\% & 97.18\% \\ \hline
[231] & 2023 & IEEE Dataport & Deep ANN, LSTM, CNN, and hybrid CNN with LSTM & 98.86\% & 99.13\% & 99.42\% & 90.83\% \\ \hline
[232] & 2022 & Cleveland & Stochastic Gradient Descent & & & \\ Classifiers, LR, SVM, NB, Conv5 GLV, and ensemble methods & 93.00\% & NP & NP & NP \\ \hline
[233] & 2022 & IEEE Dataport & NN, MLPNN, AB, SVM, LR, ANN, RF & 93.39\% & NP & NP & NP \\ \hline
[234] & 2022 & Cleveland & NB, SVM, LR, DT, RF, and KNN & 94.1\% & 97.1\% & 94.8\% & 90.8\% \\ \hline
[235] & 2022 & Cleveland & NB, DT, LR KNN, SVM, GB, and RF algorithms & 85.18\% & 0.83\% & 90\% & 86\% \\ \hline
[236] & 2022 & Cleveland and & NB with weighted approach, 2 SVMs & & & \\  & Statlog & & with XGBoost, an improved SVM & 95.9\% & 97.1\% & 94.67\% & 95.35\% \\  & & & (DOM) technique, and an XGBoost & & & & \\ \hline
[237] & 2022 & Heart disease & Stacking-Based Ensemble Learning & & & & \\  & Dataset (IEEE & (XGB, ETs, RF, GB) & 92.34\% & 92.00\% & 93.49\% & 92.74\% \\ \hline
[238] & 2021 & PhysioNet’s arrhythmia & SVM, KNN, RF, ETs, Bagging, DT, LR, and Adaptive Boosting & 99.8\% & 100\% & 100\% & 100\% \\ \hline
[238] & 2021 & UCI’s Arrhythmia & SVM, KNN, RF, ETs, Bagging, DT, LR, and Adaptive Boosting & 95.6\% & 93\% & 93\% & 93\% \\ \hline
[239] & 2021 & Framingham & MaLoDD using ensemble algorithm (10 fold) & 99.1\% & NP & NP & NP \\ \hline
[239] & 2021 & Cardiovascular Disease dataset & MaLoDD using ensemble algorithm (10 fold) & 98.0\% & NP & NP & NP \\ \hline
[148] & 2021 & Cleveland & RF, DT, and hybrid model between & & & \\  & & RF and DT & & & & \\ \hline
[240] & 2021 & Cleveland, & & & & \\  & Hungary, & & Hybrid classifiers like (DTBM), & & & \\  & Switzerland, and & (RFBM), (KNNBM), (ABBM), (GBBM) & 99.05\% & 99\% & 98\% & 99\% \\  & & and Statlog & & & & \\ \hline
[239] & 2021 & Cleveland & MaLoDD using ensemble algorithm (10 fold) & 95.5\% & NP & NP & NP \\ \hline
[142] & 2021 & Comprehensive & LR, ABM1, MLP, KNN, DT, RF & 100\% & 100\% & 100\% & 100\% \\ \hline
[140] & 2020 & StatLog & Two-tier ensemble PSO-based feature selection & 93.55\% & NP & NP & 91.67\% \\ \hline
[140] & 2020 & Hungarian & Two-tier ensemble PSO-based feature selection & 91.18\% & NP & NP & 90.91\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._