## 5 Evaluation Metrics and Cross-Validation for Cardiovascular Disease Prediction
### Evaluation Metrics
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Evaluation Metric** & **Mathematical Equation** & **Description** \\ \hline Accuracy & \(ACC=\frac{TP+TN}{TP+TN+FP+FN}\) & Accuracy is a basic evaluation metric and represents the proportion of correct predictions out of the total predictions. While accuracy is a useful metric, it may not be sufficient for imbalanced datasets, where one class (e.g., healthy patients) dominates the other (e.g., patients with cardiovascular disease). \\ \hline Recall (Sensitivity) & \(Sensitivity=\frac{TP}{TP+FN}\) & Sensitivity measures the model’s ability to correctly identify positive cases (individuals with cardiovascular disease). High sensitivity is crucial in healthcare to minimize false negatives, ensuring that individuals with cardiovascular disease are not missed. \\ \hline Specificity & \(Specificity=\frac{TN}{TN+FP}\) & Specificity measures the model’s ability to correctly identify negative cases (individuals without cardiovascular disease). High specificity is vital to minimize false positives, reducing unnecessary interventions for individuals without cardiovascular disease. \\ \hline Precision & \(Precision=\frac{TP}{TP+FP}\) & Precision is the proportion of true positive predictions out of all positive predictions. It measures the model’s accuracy when it predicts positive cases. Higher precision indicates a lower rate of false positives. Precision is important when minimizing false positives is crucial, as in medical diagnosis, where a false positive can lead to unnecessary treatments or anxiety. \\ \hline F1-score & \(F1\)-\(score=\frac{2 TP}{ZIP+FP+FN}\) & The F1-score is the harmonic mean of the precision and recall (sensitivity). It provides a balanced measure that considers both false positives and false negatives. It is beneficial when there is an imbalance between the positive and negative classes. \\ \hline Matthews correlation coefficient (MCC) & \(MCC=\frac{(TPTN-FP)}{\sqrt{((TP+FP)(TP+FN)(TN+FP)(TN+FN))}}\) & Denotes the predictive capacity of a classifier, expressed through values ranging from \(-1\) and \(+1\). For example, an MCC classifier value of \(+1\) signifies ideal predictions, while \(-1\) indicates entirely inaccurate predictions. A value close to 0 suggests that the classifier is making predictions randomly. \\ \hline Area under the curve (AUC) & \(AUC=\frac{1}{2}(\frac{FP}{(TP+TN)}+\frac{TN}{TN+FP})\) & The AUC represents the region beneath the receiver operating characteristic (ROC) curve, a graphical depiction of the true positive rate against the false positive rate. A higher AUC value corresponds to superior model performance, with an ideal model approaching an AUC close to 1. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation metrics that are utilized to investigate the effectiveness of machine learning algorithms in cardiovascular disease prediction. addressing the imbalance datasets is essential to prevent models from becoming overly biased toward the majority class [123]. In such datasets, one class (e.g., patients with cardiovascular disease) is significantly smaller than the other (e.g., healthy patients). This can lead to challenges, such as biased models, misleading accuracy, focus on specific metrics, and resampling techniques. For example, models trained on imbalanced data may exhibit a bias toward the majority class, leading to poor performance in detecting the minority class [123]. Accuracy can be misleading in imbalanced datasets, as a model that predicts all instances as the majority class can still achieve high accuracy. In imbalanced datasets, metrics like precision, recall, and F1-score become more important as they provide insights into the model's performance on the minority class. Techniques like oversampling (increasing the size of the minority class) or undersampling (reducing the size of the majority class) can be used to address the imbalance issue.