## 2 Related Work
[15] looked at the 2003-2004 National Health and Nutrition Survey (NHANES) and physical activity data, and analyzed the missing data due to device failure in accelerometer measurement using a multiple imputation approach based on additive regression, bootstrapping, and predictive mean matching (ARBP). As a result, the most accurate ARBP model was selected and analyzed as the final model [15]. In 2017, [PERSON] and [PERSON] [16] examined electronic health records (EHRs), which are a source of important data for patient status but have a lot of missing data. In this paper, imputation of missing information using deeply learned autoencoders in the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT) showed strong performance on estimation accuracy and contributed as the most powerful predictor of disease progression [17]. In 2019, [PERSON] et al. studied remote health status monitoring used to track patients and provide early detection of disease and preventive care. Internet-of-Things (IoT) technology should solve serious problems in real exams, but it facilitates the development of these monitoring systems. Therefore, forecasting is impossible with real-time health monitoring because missing data on human health indicators ignores variability. Therefore, IoT-based systems provide a way to experiment with clinical trials, learn new data from them, and make decisions on other missing data [18]. In 2019, a variety of machine learning data imputation methods was used to compare the accuracy of the data in order to replace the data of untested CpG coverage (i.e., for most CpGs, we have missing values), sites in the Bayesian hierarchy method of clustering cells using MEthyLationInference for Single cell Analysis (Melissa) and finding posterior transition patterns between cells [19]. Another paper that makes it difficult to derive the complexity and trace levels of pollutants in the detection of unexpected compounds and chemical stability assessments for food safety assessment. Therefore, we performed missing data substitutions using Liquid Chromatography-High Resolution Mass Spectrometry(LC-HRMS) Peak Peaks, Mean-LOD, and Value Decomposition-Quantile Regression Imputation of Left-censored data(SVD-QRILC) combined with chemical measurement tools that use MTBLS752 and MTBLS74 data not explicitly stated [20]. Several studies on analyzing chronic diseases paying attention to preventing chronic diseases caused by the growing elderly population have recently been published. The amount of health care data is steadily increasing; hence, various studies using deep learning and state-of-the-art methods in several applications, including image classification [21], text analysis [22], and speech recognition [23], are being conducted for data classification. In the field of health informatics, different deep learning architectures have been proposed along with the increased volume of relevant data, including convolutional neural network [24], recurrent neural network (RNN) [25], and deep neural network (DNN) [26], which is the most commonly used deep learning architecture in studies investigating data classification [27]. As such, the deep learning algorithm is also applied for health status and disease prediction. In 2013, [PERSON] and [PERSON] [28] introduced various methods and procedures for health monitoring and biometric information and data analysis using wearable sensors. Their methods processed and validated data to ensure that the data configuration was significant to the analysis, defined attributes for the datasets, and provided methods for analysis in the health and welfare sector. Various machine learning methods are used as analysis methods. Using a machine learning method according to the data characteristics has also been suggested. In 2014, [PERSON] et al. [29] presented an improved J48 algorithm for predicting diabetes and analyzed it using the diabetes data of Pima Indians. Using a total of 768 pieces of data, they analyzed information from patients with diabetes and predicted diabetes.