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
Cite This	Sleep Quality Prediction From Wearable Device Data: A Comprehensive Analysis and Model Comparison - Hema Nagendra Sai Chanda - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.20501
DOI	https://doi.org/10.36948/ijfmr.2024.v06i03.20501
Short DOI	https://doi.org/gtvtx9

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International Journal For Multidisciplinary Research