• 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.

• CELLMED
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• v.13 no.14
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• pp.14.1-14.9
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• 2023

Administration of Scopolamine can be considered a psychopharmacological model of Alzheimer's disease (AD). We made an animal model of Alzheimer's disease (AD) by administering Scopolamine to Blab/c mice. In this study, we investigated the effects of Resplex Alpha on memory impairment and cognitive function in mice in a mouse animal model of Scopolamine-induced memory impairment. Through Y-mazed and passive avoidance behavioral assays, we observed that Resplex Alpha recovered Scopolamine-induced short-term memory and cognitive functions. The results of our study imply that Resplex Alpha may be beneficial in the prevention of Alzheimer's disease (AD).

• Journal of Korean society of Dental Hygiene
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• v.22 no.2
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• pp.107-115
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• 2022

Objectives: This study aimed to examine the effects of dietary protein intake and quality on periodontal disease in Korean adults. Methods: The data used for analysis were obtained from the 7th Korean National Health and Nutrition Examination Survey (2016-2018). Data were analyzed using chi-square and t-test. Additionally, multiple logistic regression analysis was performed to assess the association between dietary protein intake and quality and periodontal disease. Statistical significance level was set at <0.05. Results: Multiple logistic regression analysis of dietary protein intake and periodontal disease in the model adjusted for socioeconomic factors showed that were significantly related to the Q1 (odds ratio [OR]: 1.18, 95% confidence interval [CI]: 1.01-1.39). However, this correlation was not significant in the model in which all variables were corrected. Moreover, analysis of the dietary protein quality and periodontal disease in model 4, which was adjusted for socioeconomic variables, showed that were significantly related to the low score (OR: 1.13, 95% CI: 1.00-1.27). Conclusions: The results showed a significant association between periodontal disease and poor intake and quality of dietary protein in the Korean adult population.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.297-307
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• 2017

The epidemic model is used to model the spread of disease and to control the disease. In this research, we utilize SEIR model which is one of applications the SIR model that incorporates Exposed step to the model. The SEIR model assumes that a people in the susceptible contacted infected moves to the exposed period. After staying in the period, the infectee tends to sequentially proceed to the status of infected, recovered, and removed. This type of infection can be used for research in cases where there is a latency period after infectious disease. In this research, we collected respiratory infectious disease data for the Middle East Respiratory Syndrome Coronavirus (MERSCoV). Assuming that the spread of disease follows a stochastic process rather than a deterministic one, we utilized the Poisson process for the variation of infection and applied epidemic model to the stochastic chemical reaction model. Using observed pandemic data, we estimated three parameters in the SIER model; exposed rate, transmission rate, and recovery rate. After estimating the model, we applied the fitted model to the explanation of spread disease. Additionally, we include a process for generating the Exposed trajectory during the model estimation process due to the lack of the information of exact trajectory of Exposed.

• Journal of Physiology & Pathology in Korean Medicine
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• v.31 no.2
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• pp.138-144
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• 2017

The pharmacological rationale of Agastache rugosa (AR) or Pogostemon cablin (PC), which have been used in traditional Korean medicine to treat dampness pattern or syndrome in gastrointestinal tract, was investigated on the gastrointestinal disorders. In-vivo model studies that examined the effect on the gastrointestinal disorders of AR or PC were collected. They were classified into disease-induced in-vivo models or non-disease in vivo models. The target disease, animal species, induction method, administration, and outcomes (changes in morphological and histological parameter, or blood and fluid) of each study were analyzed. The therapeutic mechanism of AR or PC extract was evaluated by the induced diseases and the changes in outcomes. There were contradictory reports on gastrointestinal motility of AR or PC in disease non-disease in-vivo model. AR or PC inhibited gastrointestinal motility in disease model of increased gastrointestinal motility, while promoted motility in disease model of decreased gastrointestinal motility. AR or PC also inhibited inflammatory changes in gastrointestinal inflammation model. These results suggest that the bidirectional regulation of gastrointestinal motility and the improvement of gastrointestinal inflammatory disorders might underpin traditional therapeutic effect of AR or PC, that is effect to resolve dampness of gastrointestinal tract.

• IE interfaces
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• v.23 no.4
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• pp.365-371
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• 2010

In this study, we propose a novel remote health monitoring system to accurately predict Parkinson's disease severity using a signomial regression method. In order to characterize the Parkinson's disease severity, sixteen biomedical voice measurements associated with symptoms of the Parkinson's disease, are used to develop the telemonitoring model for early detection of the Parkinson's disease. The proposed approach could be utilized for not only prediction purposes, but also interpretation purposes in practice, providing an explicit description of the resulting function in the original input space. Compared to the accuracy performance with the existing methods, the proposed algorithm produces less error rate for predicting Parkinson's disease severity.

• Development and Reproduction
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• v.27 no.2
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• pp.57-65
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• 2023

Kidney disease affects a significant portion of the global population, yet effective therapies are lacking despite advancements in identifying genetic causes. This limitation can be attributed to the absence of adequate in vitro models that accurately mimic human kidney disease, hindering targeted therapeutic development. However, the emergence of human induced pluripotent stem cells (PSCs) and the development of organoids using them have opened up a way to model kidney development and disease in humans, as well as validate the effects of new drugs. To fully leverage their capabilities in these fields, it is crucial for kidney organoids to closely resemble the structure and functionality of adult human kidneys. In this review, we aim to discuss the potential of using human PSCs or adult kidney stem cell-derived kidney organoids to model genetic kidney disease and renal cancer.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.4109-4115
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• 2014

Background: Accurate assessment of disease progression requires proper understanding of natural disease process which is often hidden and unobservable. For this purpose, disease status should be clearly detected. But in most diseases it is not possible to detect such status. This study, therefore, aims to present a model which both investigates the unobservable disease process and considers the error probability in diagnosis of disease states. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at the Iran Cancer Institute from 1995 to 1999 were analyzed. Moreover, to estimate and assess the effect of demographic, diagnostic and clinical factors as well as medical and post-surgical variables on transition rates and the probability of misdiagnosis of relapse, a hidden Markov multi-state model was employed. Results: Classification errors of patients in alive state without a relapse ($e_{21}$) and with a relapse ($e_{12}$) were 0.22 (95% CI: 0.04-0.63) and 0.02 (95% CI: 0.00-0.09), respectively. Only variables of age and number of renewed treatments affected misdiagnosis of relapse. In addition, patient age and distant metastasis were among factors affecting the occurrence of relapse (state1${\rightarrow}$state2) while the number of renewed treatments and the type and extent of surgery had a significant effect on death hazard without relapse (state2${\rightarrow}$state3)and death hazard with relapse (state2${\rightarrow}$state3). Conclusions: A hidden Markov multi-state model provides the possibility of estimating classification error between different states of disease. Moreover, based on this model, factors affecting the probability of this error can be identified and researchers can be helped with understanding the mechanisms of classification error.

• The Journal of Korean Academic Society of Nursing Education
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• v.8 no.1
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• pp.61-71
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• 2002

purpose : The purpose of this study was to find out the influencing factors of smoking-cessation behavior of patients with coronary heart disease and to suggest the model of smoking-cessation behavior which was based on the relationship between influencing factors and then to test its fitness empirically. method : This study was based on the Theory of Reasoned Action and a hypothetical model was constructed with fifteen paths in consideration of main predictive factors of smoking-cessation behavior such as biological factor, disease-related characteristics, self-efficacy, supportive factor, environmental factor, disease-related perception factor, intention-to-quit, and psychological factor. The validity of a smoking- cessation model was tested to 264 patients with coronary heart disease by using SPSS 8.0 and Window LISREL 8.12a. results : 1. Seven of the 15 paths of smoking-cessation behavior proved to be significant. 2. The final model excluded three paths in the hypothetical model was demonstrated to be improved by $x^2$=44.31 (df=38, p=.22), Goodness of Fit Index (GFI)=.98, Adjusted Goodness of Fit Index (AGFI)=.96, Non-Normed Fit Index(NNFI)=1.00, Normed Fit Index(NFI)=1.00, and Root Mean Square Residual(RMR)=.24. 3.The smoking-cessation behavior was influenced directly by biological factor, self-efficacy, supportive factor, environmental factor, intention-to-quit, and psychological factor. The smoking-cessation behavior was accounted for 82% of variance by these factors. conclusion : although the adolescents' smoking behavior can be predicted by only smoking intention, it is hard to predict the adults' smoking-cessation behavior by only this factor. Therefore, intention-to-quit, self-efficacy, supportive factor should be improved because these are promotive factors for smoking-cessation behavior. Biological factor, environmental factor, and psychological factor are inhibitive factors, so nicotine replacement therapy is helpful to the high nicotine-dependents, and ex-smokers avoid other smokers in their environment and also patients should learn and practice the stress coping-skills.

• Health Policy and Management
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• v.29 no.3
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• pp.303-311
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• 2019

Background: Cerebrovascular disease is included in four major diseases and is a disease that has high rates of prevalence and mortality around the world. Moreover, it is a disease that requires a high cost for long-term hospitalization and treatment. This study aims to figure out the correlation between grip strength, which was presented as a simple, cost-effective, and relevant predictor of cerebrovascular disease, and cerebrovascular disease based on the results of a prior study. And furthermore, our study compared model suitability of the model to measuring grip strength and relative grip strength as a predictor of cerebrovascular disease to improve the quality of cerebrovascular disease's predictor. Methods: This study conducted an analysis based on the generalized linear mixed model using the data from the Korea Longitudinal Study of Ageing from 2006 to 2016. The research subjects consisted of 9,132 middle old age people aged 45 years or older at baseline with no missing information of education level, gender, marital status, residential region, type of national health insurance, self-related health, smoking status, alcohol use, and economic activity. The grip strength was calculated the average which measured 4 times (both hands twice), and the relative grip force was divided by the body mass index as a variable considering the anthropometric figure that affects the cerebrovascular disease and the grip strength. Cerebrovascular diseases, a dependent variable, were investigated based on experiences diagnosed by doctors. Results: An analysis of the association between grip strength and found that about 0.972 (odds ratio [OR], 0.972; 95% confidence interval [CI], 0.963-0.981) was the incidence of cerebral vascular disease as grip strength increased by one unit increase and the association between relative grip strength and cerebrovascular disease found that about 0.418 (OR, 0.418; 95% CI, 0.342-0.511) was the incidence of cerebral vascular disease as relative grip strength increased by unit. In addition, the model suitability of the model for each grip strength and relative grip strength was 11,193 and 11,156, which means relative grip strength is the better application to the predictor of cerebrovascular diseases, irrespective of other variables. Conclusion: The results of this study need to be carefully examined and validated in applying relative grip strength to improve the quality of predictors of cerebrovascular diseases affecting high mortality and prevalence.