• Korean Journal of Health Education and Promotion
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• v.30 no.5
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• pp.139-151
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• 2013

Objectives: This study investigated how income, duration of illness, social stigma, quality of sleeping, ADL and social participation related to Parkinson's disease(PD) predict depression in a conceptual model based on the International Classification of Functioning(ICF) model. Methods: The sample included 206 adults with idiopathic Parkinson's disease(IPD) attending D university hospital in B Metro-politan City. A structured questionnaire was used and conducted face-to-face interviews. The collected data were analyzed for fitness, using the AMOS 18.0 program. Results: A path analysis showed that the overall model provided empirical evidence for linkages in the ICF model. Depression was manifested by significant direct effects of social stigma(${\beta}=.20$, p<.001), quality of sleeping(${\beta}=-.40$, p<.001), ADL(${\beta}=-.20$, p<.01), and social participation(${\beta}=-.12$, p<.05), indirect effects including income(p<.05), duration of illness(p<.05). These variables explained 45.9% of variance in the prediction model. Conclusions: This model may help nurses to collect and assess information to develop intervention program for depression.

• Physical Therapy Korea
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• v.28 no.3
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• pp.177-185
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• 2021

Background: Muscle undergoes change continuously with aging. Sarcopenia, in which muscle mass decrease with aging, is associated with various diseases, the risk of falling, and the deterioration of quality of life. Obesity and sarcopenia also have a synergy effect on the disease of the older adults. Objects: This study examined the risk factors for sarcopenia, sarcopenic obesity, and sarcopenia without obesity and developed prediction models. Methods: This machine-learning study used the 2008-2011 Korea National Health and Nutrition Examination Surveys in the analysis. After data curation, 5,563 older participants were selected, of whom 1,169 had sarcopenia, 538 had sarcopenic obesity, and 631 had sarcopenia without obesity; the remaining 4,394 were normal. Decision tree and random forest models were used to identify risk factors. Results: The risk factors for sarcopenia chosen by both methods were body mass index (BMI) and duration of moderate physical activity; those for sarcopenic obesity were sex, BMI, and duration of moderate physical activity; and those for sarcopenia without obesity were BMI and sex. The areas under the receiver operating characteristic curves of all prediction models exceeded 0.75. BMI could predict sarcopenia-related disease. Conclusion: Risk factors for sarcopenia-related diseases should be identified and programs for sarcopenia-related disease prevention should be developed. Data-mining research using population data should be conducted to enhance the effectiveness of early treatment for people with sarcopenia-related diseases through predictive models.

• Korean Journal of Biological Psychiatry
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• v.30 no.1
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• pp.24-30
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• 2023

Objectives Alzheimer's disease (AD) is the most common form of dementia in older adults, damaging the brain and resulting in impaired memory, thinking, and behavior. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. The aim of our study was to identify differentially expressed genes associated with AD and combined biomarkers among them to improve AD risk prediction accuracy. Methods Machine learning methods were used to compare the performance of the identified combined biomarkers. In this study, three publicly available gene expression datasets from the hippocampal brain region were used. Results We detected 31 significant common genes from two different microarray datasets using the limma package. Some of them belonged to 11 biological pathways. Combined biomarkers were identified in two microarray datasets and were evaluated in a different dataset. The performance of the predictive models using the combined biomarkers was superior to those of models using a single gene. When two genes were combined, the most predictive gene set in the evaluation dataset was ATR and PRKCB when linear discriminant analysis was applied. Conclusions Combined biomarkers showed good performance in predicting the risk of AD. The constructed predictive nomogram using combined biomarkers could easily be used by clinicians to identify high-risk individuals so that more efficient trials could be designed to reduce the incidence of AD.

• Smart Media Journal
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• v.13 no.8
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• pp.49-57
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• 2024

In this paper, we propose an algorithm that can recommend personalized health functional foods considering diseases due to the high interest in health functional foods and the development of machine learning as society enters an aging phase. By applying the KNN algorithm, we presented a foundational framework for a platform for personalized health functional food recommendations through disease analysis, matching techniques of publicly available health functional food information, and national public data. To ensure reliable matching between diseases and health functional foods, we analyzed correlations, assessed the appropriateness and accuracy of variables for enhancing the KNN algorithm, and derived improvement directions for the proposed system through the improvement of learning models and information to be disclosed in the future.

• Journal of Integrative Natural Science
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• v.16 no.2
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• pp.61-67
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• 2023

Human body ages differently due to environmental, genetic and pathological factors. DNA methylation patterns also differs depending on various factors such as aging and several other diseases. The aging clock model, which uses these differences to predict age, analyzes DNA methylation patterns, recognizes age-specific patterns, predicts age, and grasps the speed and degree of aging. Aging occurs in everyone and causes various problems such as deterioration of physical ability and complications. Alzheimer's disease is a disease associated with aging and the most common brain degenerative disease. This disease causes various cognitive functions disabilities such as dementia and impaired judgment to motor functions, making daily life impossible. It has been reported that the incidence and progression of this disease increase with aging, and that increased phosphorylation of Aβ and tau proteins, which are overexpressed in this disease and accelerates epigenetic aging. It has also been reported that DNA methylation is significantly increased in the hippocampus and entorhinal cortex of Alzheimer's disease patients. Therefore, we calculated the biological age using the Epi clock, a pan-tissue aging clock model, and confirmed that the epigenetic age of patients suffering from Alzheimer's disease is lower than their actual age. Also, it was confirmed to slow down aging.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.97-104
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• 2017

Location prediction has been successfully utilized to provide high quality of location-based services to customers in many applications. In its usual form, the conventional type of location prediction is to predict future locations based on user's past movement history. However, as location prediction needs are expanded into much complicated cases, it becomes necessary quite frequently to make inference on the locations that target user visited in the past. Typical cases include the identification of locations that infectious disease carriers may have visited before, and crime suspects may have dropped by on a certain day at a specific time-band. Therefore, primary goal of this study is to predict locations that users visited in the past. Information used for this purpose include user's demographic information and movement histories. Data mining classifiers such as Bayesian network, neural network, support vector machine, decision tree were adopted to analyze 6868 contextual dataset and compare classifiers' performance. Results show that general Bayesian network is the most robust classifier.

• The Plant Pathology Journal
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• v.26 no.2
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• pp.198-202
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• 2010

A study was conducted to find out the relationship of physico-chemical properties (viz. organic carbon(OC), pH, electrical conductivity, nitrogen, phosphorus and potassium content) of ginger growing soil with incidence percentage of rhizome rot and wilt disease complex of ginger. Organic carbon content and pH of the ginger soil contributed significantly (93%) in the prediction of ginger rhizome rot and wilt disease complex incidence with negative correlation. Soil having weak acidic reaction with OC percent greater than 2.25 was observed to have the lower average incidence of the disease.

• ETRI Journal
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• v.46 no.1
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• pp.96-105
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• 2024

We propose a method to automatically predict Alzheimer's disease from speech data using the ChatGPT large language model. Alzheimer's disease patients often exhibit distinctive characteristics when describing images, such as difficulties in recalling words, grammar errors, repetitive language, and incoherent narratives. For prediction, we initially employ a speech recognition system to transcribe participants' speech into text. We then gather opinions by inputting the transcribed text into ChatGPT as well as a prompt designed to solicit fluency evaluations. Subsequently, we extract embeddings from the speech, text, and opinions by the pretrained models. Finally, we use a classifier consisting of transformer blocks and linear layers to identify participants with this type of dementia. Experiments are conducted using the extensively used ADReSSo dataset. The results yield a maximum accuracy of 87.3% when speech, text, and opinions are used in conjunction. This finding suggests the potential of leveraging evaluation feedback from language models to address challenges in Alzheimer's disease recognition.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.335-343
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• 2019

Healthy living is an essential element of human happiness. Quality eating provides the basis for life, and the health of livestock, which provides meat and dairy products, has a direct impact on human health. In the case of calves, diarrhea is the cause of all diseases.In this paper, we use a sensor to measure calf 's biometric data to diagnose calf diarrhea. The collected biometric data is subjected to a preprocessing process for use as meaningful information. We measure calf birth history and calf biometrics. The ontology is constructed by inputting environmental information of housing and biochemistry, immunity, and measurement information of human body for disease management. We will build a knowledge base for predicting calf diarrhea by predicting calf diarrhea through logical reasoning. Predict diarrhea with the knowledge base on the name of the disease, cause, timing and symptoms of livestock diseases. These knowledge bases can be expressed as domain ontologies for parent ontology and prediction, and as a result, treatment and prevention methods can be suggested.

• Journal of the Korean Society of Radiology
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• v.7 no.3
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• pp.191-198
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• 2013

We verified reason of pain by rotator cuff disease using shoulder sonography and found a correlation between shoulder pain and stress. To find out the accuracy of sonographic prediction of rotator cuff disease among the patients with shoulder pain we surveyed 184 patients in S hospital in Seoul Korea between January to October 2012. These patients were previously diagnosed with the torn rotator cuff, adhesive capsulitis and impingement syndrom with shoulder pain. In most times, the rotator cuff disease was diagnosed among the physical workers who use shoulder excessively and also in the women in their 50~60 years of age(144 patients, 78.3%). There were significant correlation between rotator cuff disease and the stress of pain, between sonographic prediction and pain(p<.05). There were significance between shoulder pain and stress in daily life according to result for survey of BEPSI-K(p<.05).