• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.1-12
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• 2022

The current progression in the Internet of Things (IoT) and Machine Learning (ML) based technologies converted the traditional healthcare system into a smart healthcare system. The incorporation of IoT and ML has changed the way of treating patients and offers lots of opportunities in the healthcare domain. In this view, this research article presents a new IoT and ML-based disease diagnosis model for the diagnosis of different diseases. In the proposed model, vital signs are collected via IoT-based smart medical devices, and the analysis is done by using different data mining techniques for detecting the possibility of risk in people's health status. Recommendations are made based on the results generated by different data mining techniques, for high-risk patients, an emergency alert will be generated to healthcare service providers and family members. Implementation of this model is done on Anaconda Jupyter notebook by using different Python libraries in it. The result states that among all data mining techniques, SVM achieved the highest accuracy of 0.897 on the same dataset for classification of Parkinson's disease.

• Journal of Preventive Medicine and Public Health
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• v.48 no.2
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• pp.105-110
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• 2015

Objectives: Recently, low systolic blood pressure (SBP) was found to be associated with an increased risk of death from vascular diseases in a rural elderly population in Korea. However, evidence on the association between low SBP and vascular diseases is scarce. The aim of this study was to prospectively examine the association between low SBP and mortality from all causes and vascular diseases in older middle-aged Korean men. Methods: From 2004 to 2010, 94 085 Korean Vietnam War veterans were followed-up for deaths. The adjusted hazard ratios (aHR) were calculated using the Cox proportional hazard model. A stratified analysis was conducted by age at enrollment. SBP was self-reported by a postal survey in 2004. Results: Among the participants aged 60 and older, the lowest SBP (<90 mmHg) category had an elevated aHR for mortality from all causes (aHR, 1.9; 95% confidence interval [CI], 1.2 to 3.1) and vascular diseases (International Classification of Disease, 10th revision, I00-I99; aHR, 3.2; 95% CI, 1.2 to 8.4) compared to those with an SBP of 100 to 119 mmHg. Those with an SBP below 80 mmHg (aHR, 4.5; 95% CI, 1.1 to 18.8) and those with an SBP of 80 to 89 mmHg (aHR, 3.1; 95% CI, 0.9 to 10.2) also had an increased risk of vascular mortality, compared to those with an SBP of 90 to 119 mmHg. This association was sustained when excluding the first two years of follow-up or preexisting vascular diseases. In men younger than 60 years, the association of low SBP was weaker than that in those aged 60 years or older. Conclusions: Our findings suggest that low SBP (<90 mmHg) may increase vascular mortality in Korean men aged 60 years or older.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.169-175
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• 2020

A lot of research has been conducted on a system that collects and observes patients' health information in real time using Internet of Things (IoT) technology, and cares for and supports patients based on this. However, most studies have focused on underlying diseases such as diabetes or cardiovascular disease, and research on IoT systems to cope with respiratory infectious diseases such as COVID-19 is still insufficient. In a COVID-19 situation, the purpose of using an IoT respirator may vary depending on the user. In this paper, we design a system that can adequately cope with respiratory infectious diseases such as COVID-19 by applying IoT technology to respiratory protection. We categorize IoT respirator wearers into patients, medical staff, and self-quarantine persons, and define the purpose and use case of the IoT respirator system according to each classification. The proposed IoT respirator system was designed to achieve each purpose. We developed a prototype system consisting of a smart sensor, a communication module, and a non-motorized hooded respirator to show that the proposed IoT respirator system works.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Toxicological Research
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• v.25 no.4
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• pp.217-224
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• 2009

Inhaled inorganic dusts such as coal can cause inflammation and fibrosis in the lung called pneumoconiosis. Chronic inflammatory process in the lung is associated with various cytokines and reactive oxygen species (ROS) formation. Expression of some cytokines mediates inflammation and leads to tissue damage or fibrosis. The aim of the present study was to compare the levels of blood cytokines interleukin (IL)-$1\beta$, IL-6, IL-8, tumor necrosis factor (TNF)-$\alpha$ and monocyte chemoatlractant protein (MCP)-1 among 124 subjects (control 38 and pneumoconiosis patient 86) with category of chest x-ray according to International Labor Organization (ILO) classification. The levels of serum IL-8 (p= 0.003), TNF-$\alpha$ (p=0.026), and MCP-1 (p=0.010) of pneumoconiosis patients were higher than those of subjects with the control. The level of serum IL-8 in the severe group with the small opacity (ILO category II or III) was higher than that of the control (p=0.035). There was significant correlation between the profusion of radiological findings with small opacity and serum levels of IL-$1\beta$(rho=0.218, p<0.05), IL-8 (rho=0.224, p<0.05), TNF-$\alpha$ (rho=0.306, p<0.01), and MCP-1 (rho=0.213, p<0.01). The serum levels of IL-6 and IL-8, however, did not show significant difference between pneumoconiosis patients and the control. There was no significant correlation between serum levels of measured cytokines and other associated variables such as lung function, age, BMI, and exposure period of dusts. Future studies will be required to investigate the cytokine profile that is present in pneumoconiosis patient using lung specific specimens such as bronchoalveolar lavage fluid (BALF), exhaled breath condensate, and lung tissue.

• Toxicological Research
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• v.26 no.4
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• pp.329-337
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• 2010

Inhaled inorganic dusts, such as coal, can cause inflammation and fibrosis in the lungs, known as pneumoconiosis. Diagnosis of pneumoconiosis depends on morphological changes by radiological findings and functional change by pulmonary function test (PFT). Unfortunately, current diagnostic findings are limited only to lung fibrosis, which is usually irreversibly progressive. Therefore, it is important that research on potential and prospective biomarkers for pneumoconiosis should be conducted prior to initiation of irreversible radiological or functional changes in the lungs. Analytical techniques using exhaled breath condensate (EBC) or exhaled gas are non-invasive methods for detection of various respiratory diseases. The objective of this study is to investigate the relationship between inflammatory biomarkers, such as EBC pH or fractional exhaled nitric oxide ($FE_{NO}$), and pneumoconiosis among 120 retired coal miners (41 controls and 79 pneumoconiosis patients). Levels of EBC pH and FENO did not show a statistically significant difference between the pneumoconiosis patient group and pneumoconiosis patients with small opacity classified by International Labor Organization (ILO) classification. The mean concentration of $FE_{NO}$ in the low percentage $FEV_1$ (< 80%) was lower than that in the high percentage (80% $\leq$) (p = 0.023). The mean concentration of $FE_{NO}$ in current smokers was lower than that in non smokers (never or past smokers) (p = 0.027). Although there was no statistical significance, the levels of $FE_{NO}$ in smokers tended to decrease, compared with non smokers, regardless of pneumoconiosis. In conclusion, there was no significant relationship between the level of EBC pH or $FE_{NO}$ and radiological findings or PFT. The effects between exhaled biomarkers and pneumoconiosis progression, such as decreasing PFT and exacerbation of radiological findings, should be monitored.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.204-212
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• 2023

Diabetes is a condition that can be brought on by a variety of different factors, some of which include, but are not limited to, the following: age, a lack of physical activity, a sedentary lifestyle, a family history of diabetes, high blood pressure, depression and stress, inappropriate eating habits, and so on. Diabetes is a disorder that can be brought on by a number of different factors. A chronic disorder that may lead to a wide range of complications. Diabetes mellitus is synonymous with diabetes. There is a correlation between diabetes and an increased chance of having a variety of various ailments, some of which include, but are not limited to, cardiovascular disease, nerve damage, and eye difficulties. There are a number of illnesses that are connected to kidney dysfunction, including stroke. According to the figures provided by the International Diabetes Federation, there are more than 382 million people all over the world who are afflicted with diabetes. This number will have risen during the years in order to reach 592 million by the year 2035. There are a substantial number of people who become victims on a regular basis, and a significant percentage of those people are uninformed of whether or not they have it. The individuals who are most adversely impacted by it are those who are between the ages of 25 and 74 years old. This paper reviews about various machine learning techniques used to detect diabetes mellitus.

• The Journal of Internal Korean Medicine
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• v.28 no.1
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• pp.92-96
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• 2007

Objectives : This study investigated the clinical effect of a drinking habit in acute stroke patients. Methods : 409 acute stroke patients were included from October 2005 to October 2006. Patients were hospitalized within 14 days after the onset of stroke at DongGuk University International Hospital, Kyungwon University In-cheon Oriental Medical Hospital, or Department of Cardiovascular and Neurologic Diseases (Stroke Center), Kyung Hee University Oriental Hospital. We investigated general characteristics, drinking habit, and stroke subtype by TOAST classification. Results : Among drinking subjects, hemorrhagic stroke was more frequent than ischemic stroke (odds ratio 3.04), and less in small vessel occlusion than others (odds ratio 1.84). Ischemic stroke was associated with a longer (30 yrs) drinking habit than hemorrhagic stroke. Conclusions : To acquire more concrete conclusions on this theme, we need further and larger scale research.

• Korean Journal of Acupuncture
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• v.34 no.4
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• pp.191-208
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• 2017

Objectives : The purpose of this study was to analyze the trend of pharmacopuncture in Korean patent in order to establish database for patent technology. Methods : Electronic literature searches for Korean patents related to pharmacopuncture were performed in two electronic databases (Korea Intellectual Property Right Information Service and National Digital Science Library) to June 2017. Patents that were not Korean ones, did not use medicinal herb, only described method of manufacture, or had nothing to do with pharmacopuncture were excluded in this study. The status and application date of patents, Medicinal herb, target diseases, International Patent Classification (IPC), model of experiment and extracting methods were analyzed. Results : A total of 379 patents were retrieved. Based on our inclusion/exclusion criteria, 297 patents were excluded. Of 82 included patents, 27 patents did not include experiments using pharmacopuncture, and 9 patents were invented for treating animals such as pig or calf. In IPC analysis, Bee Venom, Panax (ginseng), Angelica, and Paeoniaceae were used frequently. Musculoskeletal diseases were the most targeted diseases followed by nervous diseases. For extracting, hot water extraction, distillation extraction, and solvent extraction using alcohol, ethanol, or methanol for solvent were commonly used. Conclusions : These data are useful for inventing new patent and extending range of pharmacopuncture in clinical use, however, more systematically analyzed patent studies and pharmacopuncture-related studies for new application on various diseases are needed in further studies.

• Sleep Medicine and Psychophysiology
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• v.8 no.1
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• pp.37-44
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• 2001

Background: Sleep disorders are prevalent in the general population and in medical practice. Three diagnostic classifications for sleep disorders have been developed recently: The International Classification of Sleep Disorders (ICSD), The Diagnostic and Statistical Manual, 4th edition (DSM-IV) and The International Classification of Diseases, 10th edition (ICD-10). Few data have yet been published regarding how the diagnostic systems are related to each other. To address these issues, we evaluated the frequency of sleep disorder diagnoses by DSM-IV and ICSD and compared the DSM-IV with the ICSD diagnoses. Method: Two interviewers assessed 284 inpatients who had been referred for sleep problems in general units of Anam Hospital, holding an unstructured clinical interview with each patient and assigning clinical diagnoses using ICSD and DSM-IV classifications. Results: The most frequent DSM-IV primary diagnoses were "insomnia related to another mental disorder (61.1% of cases)" and "delirium due to general medical condition (26.8%)". "Sleep disorder associated with neurologic disorder (38.4% of cases)" was the most frequent ICSD primary diagnosis, followed by "sleep disorder associated with mental disorder (33.1%)". In comparing the DSM-IV diagnoses with the ICSD diagnoses, sleep disorder unrelated with general medical condition or another mental disorder in DSM-IV categories corresponded with these in ICSD categories. But DSM-IV "primary insomnia" fell into two major categories of ICSD, "psychophysiologic insomni" and "inadequate sleep hygiene". Of 269 subjects, 62 diagnosed with DSM-IV sleep disorder related to general medical condition or another mental disorder disagreed with ICSD diagnoses, which were sleep disorders not associated with general medical condition or mental disorder, i. e., "inadequate sleep hygiene", "environmental sleep disorder", "adjustment sleep disorder" and "insufficient sleep disorder". Conclusion: In this study, we found not only a similar pattern between DSM-IV and ICSD diagnoses but also disagreements, which should not be overlooked by clinicians and resulted from various degrees of understanding of the pathophysiology of the sleep disorders among clinicians. Non-diagnosis or mis-diagnosis leas to inappropriate treatment, therefore the clinicians' understanding of the classification and pathophysiology of sleep disorders is important.