• 대한통합의학회지
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• 제12권3호
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• pp.11-24
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• 2024

Purpose : This study aims to identify the factors influencing health-related quality of life through machine learning of the general characteristics of patients with hypertension and to provide a basis for related research on patients, such as intervention strategies and management guidelines in the field of physical therapy for health promotion. Methods : Annual data from the second Korean Health Panel (Version 2.0) from 2019 to 2020, conducted jointly by the Korea Health and Social Research Institute and the National Health Insurance Service, were analyzed (Korea Health Panel, 2024). The data used in this study was collected from January to July 2020, and the data was collected using computer-assisted face-to-face interviews. Of the 13,530 household members surveyed, 1,368 were selected as the final study participants after removing missing values from 3,448 individuals diagnosed with hypertension by a doctor. Results : The results showed that walking (P2) was the most significant factor affecting health-related quality of life in random forest, followed by perceived stress (HS1), body mass index (BMIc), total household income (TOTc), subjective health status (SRHc), marital status (Marr), and education level (Edu). Conclusion :To prevent and manage chronic diseases such as hypertension, as well as to provide customized interventions for patients in advanced stages of the disease, research should be conducted in the field of physical therapy to identify influencing factors using machine learning. Based on the findings of this study, we believe that there is a need for additional content that can be utilized in the field of physical therapy to improve the health-related quality of life of patients with hypertension, such as diagnostic assessment and intervention management guidelines for hypertension, and education on perceived stress and subjective health status.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2019년도 제60차 하계학술대회논문집 27권2호
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• pp.263-266
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• 2019

In this paper, we suggest an activity and health monitoring system to observe the status of the dogs in real time. We also propose a k-days algorithm which helps monitoring pet health status using classified activity data from a machine learning approach. One of the best machine learning algorithm is used for the classification activity of dogs. Dog health status is acquired by comparing current activity calculation with passed k-days activities average. It is considered as a good, warning and bad health status for differences between current and k-days summarized moving average (SMA) > 30, SMA between 30 and 50, and SMA < 50, respectively.

• Journal of Korea Artificial Intelligence Association
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• 제1권1호
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• pp.17-21
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• 2023

The primary objective of this research is to utilize machine learning models to analyze factors tailored to each dataset for predicting mental health conditions. The study aims to develop appropriate models based on specific datasets, with the goal of accurately predicting mental health states through the analysis of distinct factors present in each dataset. This approach seeks to design more effective strategies for the prevention and intervention of depression, enhancing the quality of mental health services by providing personalized services tailored to individual circumstances. Overall, the research endeavors to advance the development of personalized mental health prediction models through data-driven factor analysis, contributing to the improvement of mental health services on an individualized basis.

• 한국멀티미디어학회논문지
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• 제24권12호
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• pp.1614-1623
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• 2021

In this paper, the purpose is evaluation of the effect of using fractal feature in machine learning based pancreatic tumor classification. We used the data that Pancreas CT series 469 case including 1995 slice of benign and 1772 slice of malignant. Feature selection is implemented from 109 feature to 7 feature by Lasso regularization. In Fractal feature, fractal dimension is obtained by box-counting method, and hurst coefficient is calculated range data of pixel value in ROI. As a result, there were significant differences in both benign and malignancies tumor. Additionally, we compared the classification performance between model without fractal feature and model with fractal feature by using support vector machine. The train model with fractal feature showed statistically significant performance in comparison with train model without fractal feature.

• 한국안전학회지
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• 제30권5호
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• pp.108-113
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• 2015

Plastic injection moulding machine is widely used for many industrial field. It is classified into mandatory safety certification machinery in Industrial Safety and Health Act because of its high hazard. In order to prevent industrial accidents by plastic injection moulding machine, it is necessary for designer to identify hazardous factors and assess the failure modes to mitigate them. This study tabulates the failure modes of main parts of plastic injection moulding machine and how their failure has affect on the machine being considered. Failure Mode & Effect Analysis(FMEA) method has been used to assess the hazard on plastic injection moulding machine. Risk and risk priority number(RPN) has been calculated in order to estimate the hazard of failures using severity, probability and detection. Accidents caused by plastic injection moulding machine is compared with the RPN which was estimated by main regions such as injection unit, clamping unit, hydraulic and system units to find out the most dangerous region. As the results, the order of RPN is injection unit, clamping unit, hydraulic unit and system units. Barrel is the most dangerous part in the plastic injection moulding machine.

• 한국안전학회지
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• 제32권1호
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• pp.115-120
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• 2017

The aim of this study is to develop construction safety and health management cost prediction model using support vector machine (SVM). To this end, theoretical concept of SVM is investigated to formulate the cost prediction model. Input and output variables have been selected by analyzing the balancing accounts for the completed construction project. In order to train and validate the proposed prediction model, 150 data sets have been gathered from field. Effects of SVM parameters on prediction accuracy are analyzed and from which the optimal parameter values have been determined. The prediction performance tests are conducted to confirm the applicability of the proposed model. Based on the results, it is concluded that the proposed SVM model can effectively be used to predict the construction safety and health management cost.

• 한국학교보건학회지
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• 제36권3호
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• pp.113-125
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• 2023

Purpose: The purpose of this study is to develop a model for predicting mental health risk among adolescents based on health behavior information by employing a hybrid machine learning method. Methods: The study analyzed data of 51,850 domestic middle and high school students from 2022 Youth Health Behavior Survey conducted by the Korea Disease Control and Prevention Agency. Firstly, mental health risk levels (stress perception, suicidal thoughts, suicide attempts, suicide plans, experiences of sadness and despair, loneliness, and generalized anxiety disorder) were classified using the k-mean unsupervised learning technique. Secondly, demographic factors (family economic status, gender, age), academic performance, physical health (body mass index, moderate-intensity exercise, subjective health perception, oral health perception), daily life habits (sleep time, wake-up time, smartphone use time, difficulty recovering from fatigue), eating habits (consumption of high-caffeine drinks, sweet drinks, late-night snacks), violence victimization, and deviance (drinking, smoking experience) data were input to develop a random forest model predicting mental health risk, using logistic and XGBoosting. The model and its prediction performance were compared. Results: First, the subjects were classified into two mental health groups using k-mean unsupervised learning, with the high mental health risk group constituting 26.45% of the total sample (13,712 adolescents). This mental health risk group included most of the adolescents who had made suicide plans (95.1%) or attempted suicide (96.7%). Second, the predictive performance of the random forest model for classifying mental health risk groups significantly outperformed that of the reference model (AUC=.94). Predictors of high importance were 'difficulty recovering from daytime fatigue' and 'subjective health perception'. Conclusion: Based on an understanding of adolescent health behavior information, it is possible to predict the mental health risk levels of adolescents and make interventions in advance.

• 한국환경보건학회지
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• 제27권1호
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• pp.14-19
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• 2001

The physical and chemical transformation and reduction degree of food waste were investigated in a food waste reduction machine using thermophilic bacteria. The first operation of the reduction machine for grain, vegetables, fishes and flesh wastes proceeded during three weeks. The first and second reduction percentages of the wastes were 98.3% and 93.2%, respectively. The residue of food waste was composed of fruits, fish, and vegetables. The temperature distribution of the reduction machine ranged between 30 and 6$0^{\circ}C$ appropriate for growth of thermophilic bacteria. At initial stage the pH in the reduction machine decreased with organic acids produced, but increased as the organic acids decomposed by different thermophilic bacteria. In the reduction machine, the moisture content of the food waste was reduced from 80-90% to 10-20% after 24 hours, and the salinity of residue was 0.29% after the second operation. The degree of odor was most high between 2 and 4 hours.

• 인터넷정보학회논문지
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• 제14권6호
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• pp.19-23
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• 2013

본 논문은 인터넷을 이용한 원격 기계상태 모니터링 시스템을 구현하고 실험하였다. 인터넷을 이용한 원격 기계상태 모니터링 시스템은 일반 회사에서 많은 비용과 인력이 소모되는 것을 효율적으로 관리할 수 있어 비용과 시간이 절약되는 장점이 있다. 본 논문에서는 기계상태 모니터링을 위한 프로토콜과 응용 프로그램 및 기계상태 측정 단말기를 개발하여 실험하고 그 결과에 대해 논의한다. 본 연구는 향 후 인터넷 원격 기계상태 모니터링 시스템에 대한 발전에 기여 할 것으로 생각된다. 마지막으로 본 논문에서 제안한 시스템이 실험을 통해 좋은 성능을 보여 주었고 또한 상용화의 가능성을 제시해 주었다.

• 대한안전경영과학회지
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• 제24권4호
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• pp.41-47
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• 2022

A reliable prediction model of national occupational accident fatality rate can be used to evaluate level of safety and health protection for workers in a country. Moreover, the socio-economic aspects of occupational accidents can be identified through interpretation of a well-organized prediction model. In this paper, we propose a machine learning based relative comparison methods to predict and interpret a national occupational accident fatality rate based on socio-economic indicators. First, we collected 29 years of the relevant data from 11 developed countries. Second, we applied 4 types of machine learning regression models and evaluate their performance. Third, we interpret the contribution of each input variable using Shapley Additive Explanations(SHAP). As a result, Gradient Boosting Regressor showed the best predictive performance. We found that different patterns exist across countries in accordance with different socio-economic variables and occupational accident fatality rate.