<![CDATA[This study focuses on using machine learning algorithms to detect cardiovascular diseases, addressing the critical need for accurate and timely diagnosis of these conditions, which are significant contributors to global morbidity and mortality. The research aims to evaluate the performance of various machine learning algorithms such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting in categorizing patients into 'yes' or 'no' groups for cardiovascular diseases based on a thorough dataset. The methodology includes data preprocessing, feature selection, and model training and assessment. The results indicate that CNN and SVM demonstrate strong and balanced performance, whereas the Decision Tree shows high sensitivity but potential overfitting. These outcomes offer valuable insights for algorithm selection and model improvement in the detection of cardiovascular diseases, setting the groundwork for further research to enhance diagnostic accuracy, clinical relevance, and healthcare outcomes.]]>
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