基于k均值和随机森林算法的睡眠阶段和质量测量

2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2023-06-16 DOI:10.1109/AINIT59027.2023.10212725

Jiahui Li, X. Ge, Lixiang Hu, Qiuhua Zhu, Zhiwen Zhang, Fuheng Lv

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引用次数: 0

摘要

本研究的目的是在大数据环境下基于k-means和随机森林算法测量睡眠质量。首先，使用k-means算法将用户的睡眠数据聚类到不同的睡眠阶段。然后，根据聚类结果，采用随机森林算法对睡眠健康指标进行预测。基于睡眠阶段的健康预测具有较高的准确性和可靠性。我们的研究为用户更好地了解自己的睡眠质量和健康状况提供了一种新的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Measuring Sleep Stages and Quality Based on K-Means and Random Forest Algorithms

The purpose of this study is to measure sleep quality based on k-means and random forest algorithms in a big data environment. Firstly, the sleeping data of users is clustered into different stages of sleep using the k-means algorithm. Then, the random forest algorithm is used to predict sleep health indicators based on the clustering results. Health prediction based on sleeping stage is of high accuracy and reliability. Our study provides a new method for users to better understand their sleep quality and health status.

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2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)

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