## 2 The Role of Machine Learning in Cardiovascular Disease Prediction
### Background
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Machine Learning Models** & **Strengths** & **Weaknesses** \\ \hline AdaBoost: Combines several weak classifiers into a unified and robust classifier. Its mechanism involves assigning higher weights to samples pose greater classification challenges while assigning lower weights to well-categorized samples. It finds application in both categorization and regression analysis. & 1. Hindles noisy data but sensitive to outliers or mislabeled instances. 2. Computationally intensive with a large number of weak learners, which may affect training time and resource requirements. 3. Struggles with complex relationships or dependencies in datasets, as it relies on relatively simple weak learners. & 1. Handles noisy data but sensitive to outliers or mislabeled instances. 2. Computationally intensive with a large number of weak learners, which may affect training time and resource requirements. \\ \hline Naive Bayes: A probabilistic model that works based on the Bayes theorem. Given the class title, it assumes that features are conditionally independent. It calculates the probability that a patient has cardiovascular disease given their feature values, such as age, cholesterol, and blood pressure. & 1. Computationally efficient with high-dimensional data. 2. Performs well with small to moderately-sized datasets. 3. Its probabilistic nature allows for the simple interpretation of results. 4. Handles a large number of features efficiently. & 3. Struggles with complex relationships or dependencies in datasets, as it relies on relatively simple weak learners. \\ \hline Deep Learning: It employs various evaluation criteria, including accuracy and specificity, to guide feature extraction inversely. Involves the use of artificial neural networks with multiple hidden layers to learn complex representations from data. Can identify intricate patterns that may be challenging for human interpretation, leading to more accurate diagnosis of diseases. & 1. Automatically discovers relevant features for data, reducing the requirement for manual feature engineering. & 1. Assumes feature independence, limiting accuracy in capturing complex dependencies among features. 2. May not capture intricate relationships between features, which can affect predictive accuracy. \\ \hline Deep Learning: It employs various evaluation criteria, including accuracy and specificity, to guide feature extraction inversely. Involves the use of artificial neural networks with multiple hidden layers to learn complex representations from data. Can identify intricate patterns that may be challenging for human interpretation, leading to more accurate diagnosis of diseases. & 1. Automatically discovers relevant features for data, reducing the requirement for manual feature engineering. & 1. Complex and computationally intensive. 2. Requires substantial computational resources for training and inference. 3. Relies on large amounts of labeled data for effective training. & 1. Complex and computationally intensive. 2. Requires substantial computational resources for training and inference. 3. Relies on large amounts of labeled data for effective training. & 2. Requires substantial computational resources for training and inference. 3. Relies on large amounts of labeled data for effective training. & 3. Relies on large amounts of labeled data for effective training. 4. Challenging to interpret, limiting utility in medical diagnostics, where interpretability is essential. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._long-duration ECG signals in the Arrhythmia dataset. The work in [122] utilized an adaptive implementation of CNNs to classify the Arrhythmia dataset, resulting in a rapid and precise patient-specific ECG classification and monitoring system. The work in [123] proposed a random forest and CNN algorithms for cardiovascular disease prediction. To this end, several imbalance techniques have been discussed.