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Health Sciences › Medicine › Cardiology and Cardiovascular Medicine
Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques
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Overview
Paper Summary
Conflicts of Interest
Identified Weaknesses
Rating Explanation
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Paper Summary
Paperzilla title
Bagging the Best: RFBM and Relief Ace Heart Disease Prediction
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.
Possible Conflicts of Interest
None identified
Identified Weaknesses
Dependency on Relief
The reliance on Relief for feature selection limits the generalizability of the model to other datasets or feature selection methods.
Sensitivity to Missing Values
A high level of missing values in other datasets could negatively impact the model's performance if not handled properly.
Limited Dataset Size
While the dataset used was large, an even larger dataset would likely improve the model's precision and generalizability.
Rating Explanation
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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Topic Hierarchy
File Information
Original Title:
Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques
File Name:
09333574.pdf
[download]
File Size:
3.17 MB
Uploaded:
July 14, 2025 at 05:08 PM
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