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This study investigates the automated detection of congestive heart failure (CHF) using short-term heart rate variability (HRV) segments through a hybrid feature selection approach. The proposed method effectively reduces the number of features while maintaining high classification accuracy, achieving up to 90.7% accuracy with only a few informative HRV features. The results indicate that this methodology could be beneficial for early screening of CHF and similar diagnostic challenges in biomedicine.

Author

Blanca Andreo

Language

EN