• 디지털정책학회지
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• 제1권2호
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• pp.61-69
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• 2022

논문은 광용적맥파(photoplethysmography, PPG) 센서에서 획득한 생체 신호, ICT 기술 및 데이터 기반의 혈당수치 예측 모델을 개발하고 검증하는 연구이다. 혈당 예측은 기계학습의 MLP 아키텍처를 이용하였다. 기계학습 모델의 입력층은 심박수, 심박변이도, 나이, 성별, VLF, LF, HF, SDNN, RMSSD, PNN50의 10개의 입력노드와 은닉층은 5개로 구성된다. 예측모델의 결과는 MSE=0.0724, MAE=1.1022 및 RMSE=1.0285이며, 결정계수(R²)는 0.9985이다. 비채혈방식으로 디지털기기에서 수집한 생체신호 데이터와 기계학습을 활용한 혈당 예측 모델을 수립하고 검증하였다. 일상에 적용하기 위해 다양한 디지털 기기의 기계학습 데이터셋 표준화와 정확성을 높이는 연구가 이어진다면 개인의 혈당 관리에 대안적 방법이 될 수 있을 것이다.

• 사상체질의학회지
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• 제33권4호
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• pp.1-9
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• 2021

Objectives The purpose of this study was to check whether the classification of the individual's Body Mass Index (BMI) could be predicted by analyzing the voice data constructed at the Korean medicine data center (KDC) using machine learning. Methods In this study, we proposed a convolutional neural network (CNN)-based BMI classification model. The subjects of this study were Korean adults who had completed voice recording and BMI measurement in 2006-2015 among the data established at the Korean Medicine Data Center. Among them, 2,825 data were used for training to build the model, and 566 data were used to assess the performance of the model. As an input feature of CNN, Mel-frequency cepstral coefficient (MFCC) extracted from vowel utterances was used. A model was constructed to predict a total of four groups according to gender and BMI criteria: overweight male, normal male, overweight female, and normal female. Results & Conclusions Performance evaluation was conducted using F1-score and Accuracy. As a result of the prediction for four groups, The average accuracy was 0.6016, and the average F1-score was 0.5922. Although it showed good performance in gender discrimination, it is judged that performance improvement through follow-up studies is necessary for distinguishing BMI within gender. As research on deep learning is active, performance improvement is expected through future research.

• Journal of Korean Biological Nursing Science
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• 제26권1호
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• pp.26-40
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• 2024

Purpose: The aim of this study was to predict the subgroups vulnerable to poorer health-related quality of life (HRQoL) according to gender in older adults. Methods: Data from 5,553 Koreans aged 65 or older were extracted from the Korea National Health and Nutrition Examination Survey. HRQoL was assessed using the EQ-5D tool. Complex sample analysis and decision-tree analysis were conducted using SPSS for Windows version 27.0. Results: The mean scores of the EQ-5D index were 0.93 ± 0.00 in men and 0.88 ± 0.00 in women. In men, poorer HRQoL groups were identified with seven different pathways, which were categorized based on participants' characteristics, such as restriction of activity, perceived health status, muscle exercise, age, relative hand grip strength, suicidal ideation, the number of chronic diseases, body mass index, and income status. Restriction of activity was the most significant predictor of poorer HRQoL in elderly men. In women, the poorer HRQoL groups were identified with nine different pathways, which were categorized based on participants' characteristics, such as perceived health status, restriction of activity, age, education, unmet medical service needs, anemia, body mass index, relative hand grip, and aerobic exercise. Perceived health status was the most significant predictor of poorer HRQoL in elderly women. Conclusion: This study presents a predictive model of HRQoL in older adults according to gender and can be used to detect individuals at risk of poorer HRQoL.

• Journal of Preventive Medicine and Public Health
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• 제32권1호
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• pp.60-64
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• 1999

환경오염과 관련된 건강효과 연구에서 흡연, 직업 등 교란변수의 영향을 효과적으로 배제할 수 있으면서 폐기능검사가 제대로 시행될 수 있는 연구대상으로 선호되고 있는 특정 연령층인 12세 학동의 보다 정확한 FVC와 FEV1 예측식을 만 들고자 전국 11개 중학교의 학생들(남자 256명, 여자 301명)을 대상으로 측정된 신장, 체중, 그리고 폐기능검사 값으로 신장-체중의 회귀식을 유도하였고, 이를 통해 12세 인구의 신장별 표준체중을 산출하였다. 이 표준체중과 실측체중의 차이인 잔차를 독립변수로 하여 폐기능 예측 식을 남녀별로 만들었는데, 남자의 경우는, FVC(ml) = 50.84 $\times$ 신장(cm) + 7.06 $\times$ 체중 잔차 - 4838.86, FEV1(m1) = 43.57 $\times$ 신장(cm) + 3.16 $\times$ 체증 잔차 4156.66 이었다. 여자에서는 FVC(ml) = 42.57 $\times$ 신장(cm) + 12.50 $\times$ 체중 잔차 - 3862.39, 그리고 FEV1(ml) = 36.29 $\times$ 신장(cm) + 7.74 $\times$ 체중 잔차 - 3200.94 이었다. 이렇게 얻어진 예측값들의 설명력(R2)은 남자에서 FVC, FEV1가 각각 0.708, 0.670이었고, 여자에서는 FVC, FEV1가 각각 0.580, 0.513이었다.

• 문화기술의 융합
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• 제8권5호
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• pp.207-218
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• 2022

본 연구는 노인의 무릎통증이 인지기능에 미치는 영향과 우울의 매개효과를 확인하기 위한 신체와 심리적 기제 간의 융합연구로 노인 인구의 성별, 연령, 학력에 따른 집단별 차이를 확인하여 치매의 위험예측 요인인 인지기능개선에 연구 목적이 있다. 분석자료는 2020년 제8차 고령화연구패널(KLoSA) 자료이며, Process macro, model 4번을 활용해 연구모형을 검증하였다. 주요 분석결과는 다음과 같다. 첫째, 우울은 무릎통증과 인지기능 간 영향관계에서 부분매개효과를 갖고 있음을 나타내었다. 둘째, 성별에 따른 우울의 매개효과는 유의하게 나타났으나, 직접효과는 남성노인이 여성노인의 두 배이며, 간접효과는 성별에 큰 차이가 없는 것으로 나타났다. 셋째, 연령에 따른 우울의 매개효과는 후기노인 집단이 전기노인 집단에 비해 영향력이 상대적으로 큰 것으로 나타났다. 넷째, 학력 구분에 따른 우울의 매개효과는 대학교 졸업 이상 집단의 경우 매개효과는 유의하지 않은 것으로 나타났으나 나머지 3개 하위 집단은 유의하게 나타났다. 본 검증결과를 토대로 노인의 인지기능 개선을 위해서는 무릎통증과 우울관리에 대한 성별, 연령, 학력에 따른 집단별 차이를 중심으로 적극적 개입 전략이 필요하다는 시사점을 도출할 수 있다.

• 보건행정학회지
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• 제33권4호
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• pp.450-456
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• 2023

Background: This study aims to develop a "Predictive Model for the Possibility of Collection Delinquent Health Insurance Contributions" for the National Health Insurance Service to enhance administrative efficiency in protecting and collecting contributions from livelihood-type defaulters. Additionally, it aims to establish customized collection management strategies based on individuals' ability to pay health insurance contributions. Methods: Firstly, to develop the "Predictive Model for the Possibility of Collection Delinquent Health Insurance Contributions," a series of processes including (1) analysis of defaulter characteristics, (2) model estimation and performance evaluation, and (3) model derivation will be conducted. Secondly, using the predictions from the model, individuals will be categorized into four types based on their payment ability and livelihood status, and collection strategies will be provided for each type. Results: Firstly, the regression equation of the prediction model is as follows: phat = exp (0.4729 + 0.0392 × gender + 0.00894 × age + 0.000563 × total income - 0.2849 × low-income type enrollee - 0.2271 × delinquency frequency + 0.9714 × delinquency action + 0.0851 × reduction) / [1 + exp (0.4729 + 0.0392 × gender + 0.00894 × age + 0.000563 × total income - 0.2849 × low-income type enrollee - 0.2271 × delinquency frequency + 0.9714 × delinquency action + 0.0851 × reduction)]. The prediction performance is an accuracy of 86.0%, sensitivity of 87.0%, and specificity of 84.8%. Secondly, individuals were categorized into four types based on livelihood status and payment ability. Particularly, the "support needed group," which comprises those with low payment ability and low-income type enrollee, suggests enhancing contribution relief and support policies. On the other hand, the "high-risk group," which comprises those without livelihood type and low payment ability, suggests implementing stricter default handling to improve collection rates. Conclusion: Upon examining the regression equation of the prediction model, it is evident that individuals with lower income levels and a history of past defaults have a lower probability of payment. This implies that defaults occur among those without the ability to bear the burden of health insurance contributions, leading to long-term defaults. Social insurance operates on the principles of mandatory participation and burden based on the ability to pay. Therefore, it is necessary to develop policies that consider individuals' ability to pay, such as transitioning livelihood-type defaulters to medical assistance or reducing insurance contribution burdens.

• 한국전문물리치료학회지
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• 제18권3호
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• pp.76-84
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• 2011

The purpose of this study was to investigate the correlations between pain intensity, physical impairments, disability, and psychological factors according to the difference in duration of low back pain. This study was a cross-sectional survey of 102 participants with low back pain, divided into two groups equal in number: The first group consisted of patients with acute and subacute low back pain, while the second group consisted of patients suffering from chronic low back pain. The results showed that gender, age, pain intensity, physical impairment, disability and Fear-Avoidance Beliefs (FABs) for work activities were not significantly different between two groups. FABs for physical activities of the first group were significantly more prevalent than in the second group. More than moderate correlations were found between pain intensity, physical impairment, and disability in the first group. Less than moderate correlations were found between pain intensity, physical impairment, disability, FABs, and depression in the second group. These findings suggest that we must consider psychological factors in the treatment of patients with chronic low back pain. Regression analyses revealed that pain intensity and FABs for work activities significantly contributed to the prediction of disability in the first group. Also, pain intensity and FABs for physical activities significantly contributed to the prediction of disability in the second group. Pain intensity was most important predictor of disability in two groups.

• 기본간호학회지
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• 제18권3호
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• pp.317-327
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• 2011

Purpose: The purpose of this study was to contribute data toward prevention from advancing dementia and also prevention of deterioration in cognitive impairment by constructing an optimal prediction model and verifying factors influencing cognitive impairment in elders with dementia who reside at home. Methods: The participants in this study were 351 elders who were registered at dementia day care centers in 11 regions of Metropolitan Incheon. Collected data were analyzed using SPSS Statistics 17.0 and SAS 9.1. Bootstrap method using the Clementine program 12.0 was applied to build an optimum prediction model. Results: Gender and education (general characteristics), alcohol, urinary/fecal incontinence, exercise, weight, and ADL (state of health), and depression (psychological state) were found to have an affect on cognitive impairment in these elders. Conclusion: Study results indicate nine key factors that affect cognitive impairment of elders with dementia who reside at home and that could be useful in prevention and management nursing plans. These factors could also be used to expand the role of nurses who are working in community day care centers, and can be applied in the development and provision of various programs to aid retention and improve cognitive function as well as preventing deterioration of cognition.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.103-111
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• 2021

Obesity affects individuals of all gender and ages worldwide; consequently, several studies have performed great works to define factors causing it. This study develops an effective method to trace obesity levels based on supervised data mining techniques such as Random Forest and Multi-Layer Perception (MLP), so as to tackle this universal epidemic. Notably, the dataset was from countries like Mexico, Peru, and Colombia in the 14- 61year age group, with varying eating habits and physical conditions. The data includes 2111 instances and 17 attributes labelled using NObesity, which facilitates categorization of data using Overweight Levels l I and II, Insufficient Weight, Normal Weight, as well as Obesity Type I to III. This study found that the highest accuracy was achieved by Random Forest algorithm in comparison to the MLP algorithm, with an overall classification rate of 96.7%.

• 산업경영시스템학회지
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• 제44권4호
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• pp.53-64
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• 2021

Depression is one of the most important psychiatric disorders worldwide. Most depression-related data mining and machine learning studies have been conducted to predict the presence of depression or to derive individual risk factors. However, since depression is caused by a combination of various factors, it is necessary to identify the complex relationship between the factors in order to establish effective anti-depression and management measures. In this study, we propose a methodology for identifying and interpreting patterns of depression expressions using the method of deriving random forest rules, where the random forest rule consists of the condition for the manifestation of the depressive pattern and the prediction result of depression when the condition is met. The analysis was carried out by subdividing into 4 groups in consideration of the different depressive patterns according to gender and age. Depression rules derived by the proposed methodology were validated by comparing them with the results of previous studies. Also, through the AUC comparison test, the depression diagnosis performance of the derived rules was evaluated, and it was not different from the performance of the existing PHQ-9 summing method. The significance of this study can be found in that it enabled the interpretation of the complex relationship between depressive factors beyond the existing studies that focused on prediction and deduction of major factors.