Key result
Machine learning models achieve >90% accuracy for cardiovascular disease diagnosis in systematic review.
Why the study?
Early detection and prediction of cardiovascular diseases are crucial to reduce morbidity and mortality, and machine learning methods have shown promising results in this area.
Can machine learning approaches accurately predict and diagnose cardiovascular diseases based on clinical risk factors?
Systematic Review
Can machine learning approaches accurately predict and diagnose cardiovascular diseases based on clinical risk factors?
Machine learning algorithms show promising accuracy for early cardiovascular disease diagnosis, but require validation on larger, more diverse real-world clinical datasets before widespread adoption.
May support ML-assisted CVD diagnosis; extends prior evidence on random forest and decision tree accuracy but requires prospective validation.
Diseases (CVD) are the world's biggest health issue and the leading cause of mortality, after cancer and diabetes. Early detection and prediction of CVDs are crucial for addressing the issue because they can drastically lower rates of morbidity and mortality. Physicians can diagnose a variety of cardiac conditions, including heart failure and valve dysfunction, with the use of computer-aided procedures. We live in the "information age," when millions of bytes of data are created daily. By employing the data mining technique, we can transform this data into knowledge for clinical research. Based on various risk factors, machine learning algorithms have demonstrated encouraging outcomes in the prediction of heart disease. Our goal in this study is to evaluate and analyze the results produced by machine learning methods, such as support vector machines, artificial neural networks, logistic regression, random forests, and decision trees, in order to predict CVDs. The accuracy of several machine learning algorithms in predicting cardiac issues is highlighted in this literature review, which can also serve as a foundation for developing a clinical decision-making tool to identify and stop heart illness early.
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Noor-Ul-Ain et al. (2025) conducted a systematic review in Cardiovascular Disease. Machine learning algorithms was evaluated on Prediction accuracy of cardiovascular diseases. Machine learning algorithms, particularly Random Forest and Decision Tree models, demonstrated high effectiveness for cardiovascular disease diagnosis, frequently achieving over 90% predictive accuracy across multiple reviewed studies.
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