Improving the heart disease detection and patients' survival using supervised infinite feature selection and improved weighted random forest
Heart disease is the leading cause of death worldwide. A Machine Learning (ML) system can detect heart disease in the early stages to mitigate mortality rates based on clinical data. However, the class imbalance and high dimensionality issues have been a persistent challenge in ML, preventing accura...
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Main Authors: | , , , , , |
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Format: | Article |
Published: |
Institute of Electrical and Electronics Engineers
2022
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Online Access: | http://eprints.um.edu.my/42085/ |
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