• International journal of advanced smart convergence
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• v.9 no.2
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• pp.1-7
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• 2020

As the demand for health screening increases, there is a need for efficient design of screening items. We build machine learning models for health screening and recommend screening items to provide personalized health care service. When offline, a synthetic data set is generated based on guidelines and clinical results from institutions, and a machine learning model for each screening item is generated. When online, the recommendation server provides a recommendation list of screening items in real time using the customer's health condition and machine learning models. As a result of the performance analysis, the accuracy of the learning model was close to 100%, and server response time was less than 1 second to serve 1,000 users simultaneously. This paper provides an adaptive and automatic recommendation in response to changes in the new screening environment.

• Korean Journal of Occupational Health Nursing
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• v.30 no.4
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• pp.145-155
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• 2021

Purpose: The purpose of this study was to examine the effects of customer violence experiences, protection systems, and monitoring systems on the subjective health status of salespersons and electronic machine repairers. Methods: A total of 934 persons were sampled nationwide, including 582 salespersons and 352 electronic machine repairers, from March 2~30, 2020 and asked to fill out a self-reported questionnaire. Results: The findings show that electronic machine repairers were more exposed to customer violence and had a weaker protection system than salespersons. They also experienced severe control from management through a monitoring system. The regression analysis revealed that verbal violence had a negative impact on the subjective health status of electronic machine repairers (p=.021). A worker protection system had significant effects on the improved subjective health status of salespersons (p=.009). Depression and fatigue had negative impacts on the subjective health status of both salespersons (depression: p<.001, fatigue: p<.001) and electronic machine repairers (depression: p<.001, fatigue: p=.002). Conclusion: These findings put a greater emphasis on the need for worker protection systems to prevent workplace violence and a health promotion program to manage depression and fatigue in workplaces.

• Safety and Health at Work
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• v.12 no.1
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• pp.51-56
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• 2021

Background: Neck pain is a major public health problem among sewing machine operators working in textile factories. Even though the textile industries are growing in number in Ethiopia, but there is a dearth of published studies on the prevalence of neck pain. Therefore, this study was aimed to assess the prevalence and associated factors of neck pain among sewing machine operators of garment factories in Mekelle city. Method: An institutional-based cross-sectional study design was implemented among 297 sewing machine operators' working in garment factories in Mekelle city. A systematic random sampling technique was used. Data were collected through interviews and analyzed using Statistical Package for Social Science version 23. Finally, variables with 95% confidence interval (CI): p < 0.05 in the multivariate analysis were significantly declared. Results: Two hundred ninety-seven sewing machine operators were enrolled, with 98.7% response rates. In this study, the 12-month prevalence rate of neck pain was found to be 42.3% (95% CI: 36.6%-47.9%), and variables like such as break time [adjusted odds ratio (AOR): 5.888, 95% CI: (2.775-12.493)], working hours per day [AOR: 6.495, 95% CI: (2.216-19.038)], static posture [AOR: 4.487, 95% CI (1.640-12.275)], and repetitive activity [AOR: 4.519, 95% CI:(2.057-9.924)] were associated with neck pain. Conclusion: In this study, neck pain is a major public health problem. Continuous work without break time, working greater than 8 hours per day, sitting in the same position for greater than 2 hours, and high repetitive activities were found significantly associated with neck pain. Owners and governmental bodies need to focus on developing preventive strategies and safety guidelines.

• Psychiatry investigation
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• v.15 no.11
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• pp.1030-1036
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• 2018

Objective In this study, we aimed to develop a model predicting individuals with suicide ideation within a general population using a machine learning algorithm. Methods Among 35,116 individuals aged over 19 years from the Korea National Health & Nutrition Examination Survey, we selected 11,628 individuals via random down-sampling. This included 5,814 suicide ideators and the same number of non-suicide ideators. We randomly assigned the subjects to a training set (n=10,466) and a test set (n=1,162). In the training set, a random forest model was trained with 15 features selected with recursive feature elimination via 10-fold cross validation. Subsequently, the fitted model was used to predict suicide ideators in the test set and among the total of 35,116 subjects. All analyses were conducted in R. Results The prediction model achieved a good performance [area under receiver operating characteristic curve (AUC)=0.85] in the test set and predicted suicide ideators among the total samples with an accuracy of 0.821, sensitivity of 0.836, and specificity of 0.807. Conclusion This study shows the possibility that a machine learning approach can enable screening for suicide risk in the general population. Further work is warranted to increase the accuracy of prediction.

• Journal of the Korean Society of School Health
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• v.10 no.2
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• pp.193-211
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• 1997

The study was designed to gain necessary basic data in order to grasp the health knowledge, attitude, and practice level of students and teachers of elementary, middle and high schools. This study was conducted through interviews of 3,400 students and 1,022 teachers attending 14 different schools large, middle and small cities and rural towns during a period of nine months (from Oct. 2 1995 to Jun. 30 1996). By the results of this study, the recommendations can be summarized as follows: 1. A school health development committee should be established of 10 members: school health related teachers (physical trainers, nurses, and teachers in charge of health), parents, persons related to health administration, local medical doctors, and student reprensentatives in order to support and immplement school health development plans. 2. Like advanced countries, a health class of 2~4 hours should beplaced in middle and high schools. A nurse majoring in health from a university should be the teacher. 3. A curriculum of health should contain the following: education on health, sex, alcohol, tabacco, the misuse of the drugs, the structure and function of human body, the growth of the body, mental health, safety and emergency care, the prevention of disease, proper eating habits and nutrition, daily health life, family health education, society health, community health, environmental pollution and individual responsibility. 4. Create a school health promotion center, with a nurse's office, and a sports center which has health machines (bars, aerobics, training, twist machine, belt massage, running machine, bench press, chest waist, hack hip extension machine) as well as a physical strength measuring machine (muscular strength, alertness, flexibility, endurance, lung functions and so on), so that the teaching staff and students can use them and train their bodies. 5. Through a refresher education program, urge teachers to understand school health promotion services. 6. Regulate a standard and establish a system of monitoring the physical enviroment of the school (the height of desks and chairs, illumination facilities, ventilation facilities, safe drinking water). 7. Create a check list of health to evaluate improvement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.16-30
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• 2023

The elderly population is increasing owing to a low fertility rate and an aging population. In addition, life expectancy is increasing, and the advancement of medicine has increased the importance of health to most people. Therefore, government and companies are developing and supporting smart healthcare, which is a health-related product or industry, and providing related services. Moreover, with the development of the Internet, many people are managing their health through online searches. The most convenient way to achieve such management is by consuming nutritional supplements or seasonal foods to prevent a nutrient deficiency. However, before implementing such methods, knowing the nutrient status of the individual is difficult, and even if a test method is developed, the cost of the test will be a burden. To solve this problem, we developed a questionnaire related to nutrient classification twice, based upon which an adaptive algorithm was designed. This algorithm was designed as a machine learning based algorithm for nutrient classification and its accuracy was much better than the other machine learning algorithm.

• Women's Health Nursing
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• v.26 no.1
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• pp.5-9
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• 2020

Artificial intelligence (AI), which includes machine learning and deep learning has been introduced to nursing care in recent years. The present study reviews the following topics: the concepts of AI, machine learning, and deep learning; examples of AI-based nursing research; the necessity of education on AI in nursing schools; and the areas of nursing care where AI is useful. AI refers to an intelligent system consisting not of a human, but a machine. Machine learning refers to computers' ability to learn without being explicitly programmed. Deep learning is a subset of machine learning that uses artificial neural networks consisting of multiple hidden layers. It is suggested that the educational curriculum should include big data, the concept of AI, algorithms and models of machine learning, the model of deep learning, and coding practice. The standard curriculum should be organized by the nursing society. An example of an area of nursing care where AI is useful is prenatal nursing interventions based on pregnant women's nursing records and AI-based prediction of the risk of delivery according to pregnant women's age. Nurses should be able to cope with the rapidly developing environment of nursing care influenced by AI and should understand how to apply AI in their field. It is time for Korean nurses to take steps to become familiar with AI in their research, education, and practice.

• Journal of Biosystems Engineering
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• v.32 no.4
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• pp.256-262
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• 2007

Image processing systems have been used to measure the plant parameters such as size, shape and structure of plants. There are yet some limited applications for evaluating plant colors due to illumination conditions. This study was focused to present adaptive methods to analyze plant leaf color regardless of illumination conditions. Color patches attached on the calibration bars were selected to represent leaf colors of lettuces and to test a possibility of health monitoring of lettuces. Repeatability of assigning leaf colors to color patches was investigated by two-tailed t-test for paired comparison. It resulted that there were no differences of assignment histogram between two images of one lettuce that were acquired at different light conditions. It supported that use of the calibration bars proposed for leaf color analysis provided color constancy, which was one of the most important issues in a video color analysis. A health discrimination equation was developed to classify lettuces into one of two classes, SOUND group and POOR group, using the machine vision. The classification accuracy of the developed health discrimination equation was 80.8%, compared to farmers' decision. This study could provide a feasible method to develop a standard color chart for evaluating leaf colors of plants and plant health monitoring system using the machine vision.

• Safety and Health at Work
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• v.6 no.4
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• pp.268-278
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• 2015

Background: This study aimed to assess the whole-body vibration (WBV) exposure among large blast hole drill machine operators with regard to the International Organization for Standardization (ISO) recommended threshold values and its association with machine- and rock-related factors and workers' individual characteristics. Methods: The study population included 28 drill machine operators who had worked in four opencast iron ore mines in eastern India. The study protocol comprised the following: measurements of WBV exposure [frequency weighted root mean square (RMS) acceleration ($m/s^2$)], machine-related data (manufacturer of machine, age of machine, seat height, thickness, and rest height) collected from mine management offices, measurements of rock hardness, uniaxial compressive strength and density, and workers' characteristics via face-to-face interviews. Results: More than 90% of the operators were exposed to a higher level WBV than the ISO upper limit and only 3.6% between the lower and upper limits, mainly in the vertical axis. Bivariate correlations revealed that potential predictors of total WBV exposure were: machine manufacturer (r = 0.453, p = 0.015), age of drill (r = 0.533, p = 0.003), and hardness of rock (r = 0.561, p = 0.002). The stepwise multiple regression model revealed that the potential predictors are age of operator (regression coefficient ${\beta}=-0.052$, standard error SE = 0.023), manufacturer (${\beta}=1.093$, SE = 0.227), rock hardness (${\beta}=0.045$, SE = 0.018), uniaxial compressive strength (${\beta}=0.027$, SE = 0.009), and density (${\beta}=-1.135$, SE = 0.235). Conclusion: Prevention should include using appropriate machines to handle rock hardness, rock uniaxial compressive strength and density, and seat improvement using ergonomic approaches such as including a suspension system.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.728-735
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• 2022

Roofers get exposed to increased risk of knee musculoskeletal disorders (MSDs) at different phases of a sloped shingle installation task. As different phases are associated with different risk levels, this study explored the application of machine learning for automated classification of seven phases in a shingle installation task using knee kinematics and roof slope information. An optical motion capture system was used to collect knee kinematics data from nine subjects who mimicked shingle installation on a slope-adjustable wooden platform. Four features were used in building a phase classification model. They were three knee joint rotation angles (i.e., flexion, abduction-adduction, and internal-external rotation) of the subjects, and the roof slope at which they operated. Three ensemble machine learning algorithms (i.e., random forests, decision trees, and k-nearest neighbors) were used for training and prediction. The simulations indicate that the k-nearest neighbor classifier provided the best performance, with an overall accuracy of 92.62%, demonstrating the considerable potential of machine learning methods in detecting shingle installation phases from workers knee joint rotation and roof slope information. This knowledge, with further investigation, may facilitate knee MSD risk identification among roofers and intervention development.