## 4 Results and Discussion
The holdout method, which involves consistently reserving a segment of the dataset for testing while utilizing the remainder for training, guarantees that model assessment accurately represents generalizability instead of overfitting to particular data divisions, hence enhancing the reliability of the provided metrics. Table 4 displays the average performance metrics--precision, recall, F1-measure, accuracy, and ROC-AUC--of various machine learning algorithms applied to the UCI Heart Disease dataset utilizing a cross-validation holdout of 20. The Artificial Neural Network (ANN) exhibits exceptional performance, achieving 95% accuracy, 95.3% precision, 94.9% recall, 94.8% F1-measure, and 98% ROC-AUC a, significantly surpassing traditional machine learning algorithms. Random Forest emerges as the most effective classical model, with 84% accuracy along with robust precision. Ensemble approaches, including Bagging, AdaBoost, and Voting Ensemble, attain moderate accuracies of approximately 80-81%, although individual models such as SVM, KNN, Logistic Regression, and Naive Bayes fall within the 80-82% accuracy range. Using the above results of the holdout, we further analyze the performance of ANN using ROC curve analysis, as shown in Figure 4. It evaluates the classification efficacy of several machine learning models, with each line illustrating the balance between true positive and false positive rates across different thresholds. The Artificial Neural Network (ANN) exhibits the greatest AUC of 0.98, signifying near-perfect class discrimination and markedly higher performance compared to the other models. The elevated AUC indicates that the ANN continuously attains an optimal balance between sensitivity and specificity, hence reducing both false positives and false negatives. Conversely, models such as Logistic Regression, Naive Bayes, and the Voting Ensemble exhibit lower AUCs of 0.72, indicating diminished overall classification efficacy. The diagonal dashed line (AUC = 0.5) signifies random chance, and the greater the distance of a model's curve below this line, the superior its predictive capability--with ANN markedly surpassing all other models in this aspect. Further, to ensure the complete efficiency of obtained results, 10-fold cross-validation is also applied, as shown in Table 5. It can be observed from the above results that the ANN model demonstrated superior performance compared to traditional classifiers, as has also been shown in other disease prediction studies [72; 73]. Despite ensemble methods typically  
Figure 3: The paired \(t\)-tests comparison of ANN’s performance against other models. performing well, ANN achieved the best balance between sensitivity and specificity after SMOTE was applied, highlighting its capacity to capture nonlinear relationships in the limited dataset. Moreover, Table 6 summarizes the training and testing times of all employed models. Bagging and RF demonstrated the fastest execution with only 2 s for training and 0.3 s for testing; however, the performance was notably low. In contrast, AdaBoost showed high computation time (61 s training, 6.1 s testing) but with comparatively lower performance. In contrast, ANN requires more time, like AdaBoost, i.e., 69 s for training and 5 s for testing, but the performance of ANN justifies its higher computational cost. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Algorithm** & **Precision (\%)** & **Recall (\%)** & **F1-Measure (\%)** & **Accuracy (\%)** & **ROC-AUC (\%)** \\ \hline AdaBoost & 80.5 & 79.8 & 79.6 & 80 & 82 \\ \hline Bagging & 81.0 & 80.3 & 80.0 & 81 & 77 \\ \hline RF & 84.0 & 83.2 & 83.4 & 84 & 83 \\ \hline KNN & 81.5 & 80.8 & 80.7 & 81 & 80 \\ \hline LR & 81.2 & 80.0 & 79.9 & 80 & 72 \\ \hline NB & 81.5 & 81.1 & 80.9 & 82 & 72 \\ \hline SVM & 83.5 & 82.0 & 81.9 & 82 & 73 \\ \hline Vote & 81.4 & 80.7 & 80.2 & 81 & 72 \\ \hline ANN & 95.3 & 94.9 & 94.8 & 95 & 98 \\ \hline \end{tabular}
\end{table}