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Measuring exact obesity rates is challenging because the existing measures, such as body mass index (BMI) and waist-to-height ratio (WHtR), do not account for various body metrics and types. Therefore, these measures are insufficient for use as health indices. This study presents a model that accurately classifies abdominal obesity, or muscular obesity, which cannot be diagnosed with BMI. Using the model, a web-based calculator was created, which provides information on obesity by predicting healthy ranges, and obesity, underweight, and overweight values. For this study, musculoskeletal mass and body composition mass data were obtained from Size Korea. The groups were divided into four groups, and six body circumference values were used to classify the obesity levels. Of the four learning models, the random forest model was used and had the highest accuracy (99%). This enabled us to build a web-based tool that can be accessed from anywhere and can measure obesity information in real-time. Therefore, users can quickly receive and update their own obesity information without using existing high-cost equipment (e.g., an Inbody machine or a body-composition analyzer), thereby making self-diagnosis convenient. With this model, it was easy to recognize and manage health conditions by quickly receiving and updating information on obesity without using traditional, expensive equipment, and by providing accurate information on obesity, according to body types, rather than information such as BMI, which are identified based on specific body characteristics.
Changgyun Kim; Sekyoung Youm. Development of a Web Application Based on Human Body Obesity Index and Self-Obesity Diagnosis Model Using the Data Mining Methodology. Sustainability 2020, 12, 3702 .
AMA StyleChanggyun Kim, Sekyoung Youm. Development of a Web Application Based on Human Body Obesity Index and Self-Obesity Diagnosis Model Using the Data Mining Methodology. Sustainability. 2020; 12 (9):3702.
Chicago/Turabian StyleChanggyun Kim; Sekyoung Youm. 2020. "Development of a Web Application Based on Human Body Obesity Index and Self-Obesity Diagnosis Model Using the Data Mining Methodology." Sustainability 12, no. 9: 3702.
In this research, an Internet of things–based smart factory was established for a die-casting company that produces automobile parts, and the effect of casting parameters on quality was analyzed using data collected from the system. Most of the die-casting industry in Korea consists of small- and medium-sized enterprises with inferior finances and skeptical views about the establishment of a smart factory. In response, the Korean government is providing various types of support to spread the implementation of smart factories for small- and medium-sized enterprises. Although small- and medium-sized enterprises have become more active in establishing smart factories according to the government policies, the effect of smart factories requires real-time monitoring. A monitoring system has been built but the data collected are not being utilized properly. Therefore, it is necessary to establish a system suitable for the die-casting environment and data analysis purposes and to utilize it to enable the analysis of data. To this end, we established to smart factory that provides data based on the Internet of things. Among the data collected, casting parameter data were analyzed through a data mining technique to establish a relationship between casting parameters and the quality of production. It is expected that a method of systematic implementation will be provided to die-casting companies that want to build smart factories in the future and that a plan for managing casting parameter by-product will be established. In addition, algorithms that can solve the problem of multi-collinearity among the casting parameters and aid in the development of new products are needed to detect optimum casting parameters.
SangWoo Park; Kim Changgyun; Sekyoung Youm. Establishment of an IoT-based smart factory and data analysis model for the quality management of SMEs die-casting companies in Korea. International Journal of Distributed Sensor Networks 2019, 15, 1 .
AMA StyleSangWoo Park, Kim Changgyun, Sekyoung Youm. Establishment of an IoT-based smart factory and data analysis model for the quality management of SMEs die-casting companies in Korea. International Journal of Distributed Sensor Networks. 2019; 15 (10):1.
Chicago/Turabian StyleSangWoo Park; Kim Changgyun; Sekyoung Youm. 2019. "Establishment of an IoT-based smart factory and data analysis model for the quality management of SMEs die-casting companies in Korea." International Journal of Distributed Sensor Networks 15, no. 10: 1.
The aim of this study was to predict chronic diseases in individual patients using a character-recurrent neural network (Char-RNN), which is a deep learning model that treats data in each class as a word when a large portion of its input values is missing. An advantage of Char-RNN is that it does not require any additional imputation method because it implicitly infers missing values considering the relationship with nearby data points. We applied Char-RNN to classify cases in the Korea National Health and Nutrition Examination Survey (KNHANES) VI as normal status and five chronic diseases: hypertension, stroke, angina pectoris, myocardial infarction, and diabetes mellitus. We also employed a multilayer perceptron network for the same task for comparison. The results show higher accuracy for Char-RNN than for the conventional multilayer perceptron model. Char-RNN showed remarkable performance in finding patients with hypertension and stroke. The present study utilized the KNHANES VI data to demonstrate a practical approach to predicting and managing chronic diseases with partially observed information.
Changgyun Kim; YoungDoo Son; Sekyoung Youm. Chronic Disease Prediction Using Character-Recurrent Neural Network in The Presence of Missing Information. Applied Sciences 2019, 9, 2170 .
AMA StyleChanggyun Kim, YoungDoo Son, Sekyoung Youm. Chronic Disease Prediction Using Character-Recurrent Neural Network in The Presence of Missing Information. Applied Sciences. 2019; 9 (10):2170.
Chicago/Turabian StyleChanggyun Kim; YoungDoo Son; Sekyoung Youm. 2019. "Chronic Disease Prediction Using Character-Recurrent Neural Network in The Presence of Missing Information." Applied Sciences 9, no. 10: 2170.