Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques
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Resumen del artículo
This study developed a hybrid Random Forest Bagging Method (RFBM) model combined with Relief feature selection for predicting heart disease. Using a combined dataset and 10 key features, the model achieved 99.05% accuracy, significantly outperforming existing models. This suggests RFBM with Relief is a promising approach for improving early diagnosis and mitigating cardiovascular disease mortality.
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Scientists found a new way for computers to guess if someone might get heart disease, and it's super good at it! This helps doctors find problems really early, like a smart detective for your heart.
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Explicación de la calificación
This paper presents a novel approach to heart disease prediction by combining multiple datasets and utilizing a hybrid RFBM model with Relief feature selection. The achieved accuracy of 99.05% is substantially higher than previous works. Though limitations exist (dependency on Relief, sensitivity to missing values, limited dataset size), the methodology is sound and the results are impactful, warranting a rating of 4.
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