## 2 The Role of Machine Learning in Cardiovascular Disease Prediction
### Background
\begin{table}
**Machine Learning Model** & **Strengths** & **Weaknesses** \\ \hline Logistic Regression (LR): A statistical model that assesses the probability of an individual having a cardiovascular disease based on various input features, such as age, cholesterol levels, and blood pressure. & 1. Simplicity and interpretability in disease diagnostics. & 1. Assumes linear input–outcome relationship. 2. Less flexible in complex medical scenarios. \\ \hline Decision Trees (DT): Applicable for both classification and regression problems. It generates a tree-like structure of decision rules by recursively splitting the data according to input attributes. Helps to determine the most important elements or symptoms of cardiovascular disease. & 1. Easy to interpret and hence can explain the decision-making process easily. 2. Handles non-linear relationships in medical data with both categorical and numerical features and identifies key factors for cardiovascular disease. & 1. Frames to overfitting, impacting generalization. 2. May not capture complex relationships as well as other models. \\ \hline Random Forest (RF): An ensemble learning method that combines multiple decision trees to make predictions. It is less prone to overfitting compared to individual decision trees, resulting in the improved generalization of new data. It provides higher predictive accuracy due to the combination of multiple trees and the reduction of bias and variance. & 1. Mitigates decision tree overfitting by averaging predictions. 2. It offers robustness, high accuracy, and handling of non-linearity. & 1. Computationally intensive, may need more resources. \\ \hline Support Vector Machines (SVMs): A powerful algorithm that can be used for both classification and regression tasks. It obtains the optimal hyperplane, which seeks the best separation of the data points into different classes. Proficiency in managing single-dimensional data; hence, it is suitable for datasets with many features. & 2. Larger model sizes can be limited in resource-constrained environments. 3. Requires proper hyperparameter tuning for optimization. \\ \hline \hline \end{tabular} \
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} Support Vector Machines (SVMs): A powerful algorithm that can be used for both classification and regression tasks. It obtains the optimal hyperplane, which seeks the best separation of the data points into different classes. Proficiency in managing single-dimensional data; hence, it is suitable for datasets with many features. & 1. Handles non-linear relationships with kernels. 2. Versatile and effective in complex pattern capture. 3. Find complex decision boundaries in high dimensions. & 1. Computationally expensive, complex in high dimensions. 2. Sensitive to kernel choice. 3. Time-consuming training on large datasets. 4. Essential hyperparameter tuning for optimal performance. \\ \hline \hline \end{tabular}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \(K\)-Nearest Neighbors (KNN): A simple algorithm that finds the \(k\)-nearest data points in the training dataset, such as the Euclidean distance. It predicts the class of the new data point by taking a majority vote from the KNN. Effective when similar patients with similar features profiles are likely to have similar cardiovascular disease outcomes. & 1. Non-parametric algorithm, adapts well to diverse and non-linear patterns of data distributions. & 1. Computationally expensive for large datasets. 2. Sensitive to the choice of \(k\). 3. May struggle with imbalanced datasets. 4. Less sensitive to outliers and noisy data points. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A summary of machine learning models in prediction of cardiovascular disease. The work in [116] developed a three-phase technique based on ANN for cardiovascular disease prediction in angina, achieving an accuracy of 88.89%. The work in [117] designed an integrated medical decision support system for cardiovascular disease diagnosis, incorporating ANN and Fuzzy AHP. The proposed method achieved an accuracy of 91.10%.