Heart disease prediction using entropy based feature engineering and ensembling of machine learning classifiers
作者：
Highlights：
• A novel framework for heart disease detection is proposed.
• Handling categorical attributes based on its importance in health care and outcome.
• Covariance-based multivariate outlier detection is used for the outlier detection.
• Proposed Entropy-based FE (EFE) has performed well with ML models.
• The proposed ML pipeline outperformed the state of art works.
摘要
•A novel framework for heart disease detection is proposed.•Handling categorical attributes based on its importance in health care and outcome.•Covariance-based multivariate outlier detection is used for the outlier detection.•Proposed Entropy-based FE (EFE) has performed well with ML models.•The proposed ML pipeline outperformed the state of art works.
论文关键词：Machine Learning,Heart disease prediction,Ensemble model,Entropy based feature engineering,Imputing Missing Values,Outlier Removal
论文评审过程：Received 22 October 2021, Revised 2 June 2022, Accepted 14 June 2022, Available online 21 June 2022, Version of Record 30 June 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117882
