## 4 Results and Discussion  
An imbalance dataset was used in the experimentation where SMOTE was applied to create a balance of classes within the dataset. Furthermore, various ML models were applied with the intention of finding the model that can best classify the given dataset. After experimentation, it was observed that SMOTE-based ANN has outperformed all other models with an accuracy of 96%. It is worth mentioning that in order to prevent data leakage and ensure valid performance estimation, SMOTE was applied only within the training folds during cross-validation rather than on the entire dataset before splitting. This prevents synthetic samples from appearing simultaneously in both training and test sets. Additionally, we included an external 20% holdout validation set to assess model generalization beyond cross-validation. In order to comprehensively assess the performance of ML algorithms, additional metrics such as precision, recall, and F1-score were utilized alongside accuracy. Table 3 depicts the evaluation results obtained after applying various ML algorithms on a balanced dataset before and after applying SMOTE. The results obtained for both classes show how SMOTE has effectively assisted in enhancing the evaluation results for the minor class, ultimately enhancing values for evaluation parameters. Moreover, to further analyze the impact of information leakage and the performance of ANN, we conducted a comprehensive comparative analysis of nine classifiers: ANN, Random Forest, SVM, KNN, Naive Bayes, Logistic Regression, AdaBoost, Bagging, and a Voting Ensemble using pairwise _t_-test. Using 30 repeated runs for each model, we calculated the average accuracy and applied paired _t_-tests comparing ANN's performance against each alternative. ANN achieved a mean accuracy of 96.1%, outperforming all other models, with the next-best Random Forest averaging 85%. Paired _t_-tests revealed statistically significant differences in performance (all _p_-values < 0.0001), confirming that ANN's superiority was not due to random variation but represents a robust, statistically validated improvement. The 95% confidence intervals for ANN's accuracy (95.2-97.0%) showed no overlap with the confidence ranges of competing models, reinforcing its leading position. Figure 3 presents boxplots illustrating the accuracy distributions across all models, clearly demonstrating ANN's dominance in both central tendency and spread. These results establish ANN as the most effective and reliable classifier for heart disease prediction within our study framework. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Average Precision**} & \multicolumn{2}{c}{**Average Recall**} & \multicolumn{2}{c}{**Average F1-Measure**} & \multicolumn{2}{c}{**Average Accuracy**} & \multicolumn{2}{c}{**ROC Value**} \\ \cline{2-10}  & **Original** & **SMOTE-** & **Applied** & **Original** & **SMOTE-** & **Applied** & **Original** & **Dataset** & **SMOTE-** & **Applied** & **Original** & **SMOTE-** \\ \hline Adaboost & 79.8 & 81.3 & 78.6 & 80.4 & 78.4 & 80.2 & 79 & 81 & 81 & 83 \\ \hline Bagging & 80.2 & 81.7 & 79.4 & 81 & 78.9 & 80.6 & 78 & 80 & 73 & 75 \\ \hline RF & 83.3 & 85.1 & 82.1 & 84.3 & 82.3 & 84.4 & 83 & 85 & 80 & 82 \\ \hline KNN & 80.9 & 82.5 & 80.2 & 81.8 & 80.1 & 81.7 & 80 & 82 & 78 & 80 \\ \hline LR & 80.2 & 81.8 & 78.9 & 80.7 & 78.8 & 80.6 & 79 & 81 & 68 & 70 \\ \hline NB & 80.5 & 81.9 & 80.1 & 81.5 & 79.8 & 81.4 & 81 & 82 & 68 & 70 \\ \hline SVM & 82.4 & 84.5 & 81 & 83 & 80.7 & 82.9 & 81 & 82 & 68 & 70 \\ \hline Vote & 80.9 & 82.3 & 80.2 & 81.5 & 79.7 & 81 & 80 & 81 & 67 & 69 \\ \hline ANN & 94.7 & 96.1 & 94.1 & 95.7 & 94.0 & 95.7 & 95 & 96 & 98 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average accuracy, precision, recall, and F1-measure of different ML algorithms applied on dataset before and after applying SMOTE. Furthermore, to ensure the reliability of results external holdout of 20% was also set for testing purposes.