Prediction of Sleep Health Status, Visualization and Analysis of Data

Yavuz Selim Taspinar, Ilkay Cinar
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Abstract

Sleep, as an indispensable element of human life, is accepted as one of the main sources of health, vitality and productivity. There are many factors that affect sleep health. Stress level, irregularity of sleep patterns and excessive use of technological devices can be given as examples. Sleep health can be determined by analyzing various variables about sleep. Sleep health can be determined by using these variables with machine learning methods. For this purpose, a dataset containing 374 rows of data and 13 features was used in this study. Sleep disorder conditions can be classified as None, Sleep Apnea, and Insomnia using 12 features. Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) and k Nearest Neighbor (kNN) methods were used for classification. Classification success was 91.66% from the RF model, 90.27% from the SVM model, 90.27% from the LR model and 87.50% from the kNN model. In order to analyze which feature is more effective in classification processes, box plot and correlation analysis methods were used. As a result of the analyzes, it was determined that the body mass index has the greatest effect on the determination of sleep disorder.
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睡眠健康状况预测、数据可视化及分析
睡眠作为人类生活不可缺少的组成部分,被认为是健康、活力和生产力的主要来源之一。影响睡眠健康的因素有很多。压力水平、睡眠模式不规律和过度使用科技设备都可以作为例子。睡眠健康可以通过分析有关睡眠的各种变量来确定。睡眠健康可以通过使用这些变量和机器学习方法来确定。为此,本研究使用了包含374行数据和13个特征的数据集。睡眠障碍状况可以用12个特征分类为无睡眠、睡眠呼吸暂停和失眠。使用随机森林(RF)、支持向量机(SVM)、逻辑回归(LR)和k近邻(kNN)方法进行分类。RF模型的分类成功率为91.66%,SVM模型为90.27%,LR模型为90.27%,kNN模型为87.50%。为了分析哪种特征在分类过程中更有效,采用了箱线图和相关分析方法。分析结果表明,体重指数对判断睡眠障碍的影响最大。
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