International Journal For Multidisciplinary Research

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

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Sleep Quality Prediction From Wearable Device Data: A Comprehensive Analysis and Model Comparison

Author(s) Hema Nagendra Sai Chanda
Country India
Abstract This study explores machine learning techniques for predicting sleep quality from wearable device data. It compares classification and regression approaches, including Random Forest, Gradient Boosting, and deep learning models like RNNs and CNNs. Results highlight the effectiveness of these models and offer insights into optimal algorithms and feature selection for accurate sleep quality prediction.
Keywords Classification, Convolutional Neural Networks, Gradient Boosting, Machine Learning, Random Forest, Regression, Recurrent Neural Networks, Sleep Quality Prediction, Wearable Devices
Field Computer
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-16
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.20501
Short DOI https://doi.org/gtvtx9
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E-ISSN 2582-2160
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