<![CDATA[Cardiovascular diseases are the leading cause of global mortality. By utilizing machine learning methods, patient data analysis can estimate risk and plan more effective interventions. The aim of this study is to evaluate the performance of algorithms such as Random Forest, Decision Tree, and Gradient Boosting in predicting cardiovascular diseases. The first step involves using the K-Nearest Neighbors (KNN) imputation method to address missing values in the dataset derived from cardiovascular disease patients. The data is split into training and testing sets with a ratio of 80:20. The three machine learning algorithms are tested on this data, with evaluations conducted using accuracy, precision, recall, and F1-score. The results show that Random Forest delivers the best performance with an accuracy of 65%, precision of 60%, recall of 31%, and F1-score of 41%. Although Decision Tree and Gradient Boosting demonstrate competitive results, they are slightly lower than those of Random Forest. The KNN imputation method is proven effective in handling missing values. In conclusion, Random Forest outperforms, followed by Gradient Boosting and Decision Tree, providing a foundation for the development of more accurate predictive models in diagnosing cardiovascular diseases.]]>
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