Optimizing Cardiovascular Disease Diagnosis with Machine Learning: An Analysis

Abstract

Cardiovascular disease (CVD) is a major global health problem, responsible for over 17 million deaths each year. Early and accurate diagnosis of CVD is critical to effective treatment and management, and machine learning algorithms have shown promise in this area. In recent years, machine learning models have been developed and applied to various healthcare problems, and have shown significant potential in the prediction and diagnosis of CVD. The purpose of this study is to evaluate the performance of a stacked machine learning model in the diagnosis of CVD. In particular, we aim to compare the performance of this model with that of recent studies in this area, and to assess the reliability and accuracy of machine learning algorithms in CVD diagnosis. The study used a publicly available dataset of patient data, including demographic, lifestyle, and medical history information, to train and test the stacked machine learning model. The model was trained using several different machine learning algorithms, including decision trees, random forests, and support vector machines, and the performance of the model was evaluated using metrics such as accuracy, Matthews correlation coefficient (MCC), and F1 score. The results of the study showed that the stacked machine learning model achieved a high level of accuracy in CVD diagnosis, with an accuracy of 0.7776785714285714, an MCC of 0.5613383240343626, and an F1 score of 0.7764597470054544. These results are more reliable than the results of a recent study, which reported an accuracy of 0.75 and an MCC of 0.5. Future research could extend the scope of this study by exploring the use of other machine learning algorithms, such as deep learning or neural networks, in the diagnosis of CVD. Additionally, the study could be extended to include a larger and more diverse patient population, in order to better understand the generalizability of the results.