International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Prediction of Human Physiological States by Using an Enhanced Recursive Feature Elimination Method

Author(s) Thangapriya, Nancy Jasmine Goldena
Country India
Abstract Human Action Recognition (HAR) is a vital part of the healthcare sector. Medical practitioners are experiencing difficulty in recognizing human physiological conditions due to the enormous quantity of sensory stimulation. Machine learning techniques help to predict human physiological conditions, providing medical practitioners to work more efficiently. Feature selection is an essential part of discovering new knowledge in the majority of real-world problems when there are a lot of features. Feature selection is extremely useful because it speeds up decisions and enhances classification performance. The importance of feature selection in machine learning is dimension reduction in a massive multivariate data collection. This paper presents an effective feature selection method known as the Enhanced Recursive Feature Elimination (EFRE) for selecting key features from the data set for HAR prediction. The experimental results reveal that the ERFE technique selects the most suitable features for HAR prediction. The performance of the proposed ERFE approach is tested using different performance evaluation metrics. The performance analysis shows that the ERFE method outperforms existing feature selection methods with 88% accuracy.
Keywords Activity recognition, Classification, ERFE, Feature Selection, Performance Evaluation
Field Computer Applications
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-06-23
Cite This Prediction of Human Physiological States by Using an Enhanced Recursive Feature Elimination Method - Thangapriya, Nancy Jasmine Goldena - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.23222
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.23222
Short DOI https://doi.org/gt2433
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E-ISSN 2582-2160
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