Earticle

한국의 노인(65세 이상) 자살 문제는 점차 심각해지고 있는 추세이다. 급격한 인구 고령화 흐름에 따라 이러한 고령층의 자살 추세가 더욱 가속화될 것으로 추정되고 있어, 노인 자살을 예방하고 감소시키는 것이 개인 뿐만 아니라 중요한 사회적 과제로 대두되고 있다. 따라서 본 연구는 한국 노인들을 대상으로 자살 생각의 원인 요인을 파악하고 예측 모델을 개발하는 것을 목적 한다. 본 연구는 한국복지패널조사에서 제공하는 7개년의 패널 데이터를 활용하였으며 자살의 대인 관계 이론(interpersonal theory of suicide)과 사회 해체 이론(social disorganization theory)을 바탕으로 노인 자살의 잠재 원인 요인들을 선정한다. 다음으로 노인의 자살 생각에 대한 원인 요인 파악을 위해 패널 로짓 모형 분석을 진행하고 노인 자살 생각의 예측 모델 개발을 위해 딥 러닝과 머신 러닝 알고리즘을 활용한다. 본 연구는 계량 모형 분석을 통해 검증한 주요 원인 요인들을 활용하여 노인 자살을 예방할 수 있는 구체적인 노인 복지 정책 수립에 기여하고자 한다. 본 연구에서 제시된 예측 모델은 자살 고위험군 노인을 선별하고 관리할 수 있는 방안 마련의 기반을 제공한다. 또한 본 연구는 혼합방법론의 시너지를 보였다는 점에서 학술적 시사점을 가진다.

Elderly suicide problem has become worse in South Korea. With a rapid aging of the population, the trend of suicide among the elderly is expected to accelerate, preventing elderly suicide has been considered an important societal problem. Thus, we aim to investigate various factors that explain suicidal ideation and to develop a predictive model for suicidal ideation in the context of elderly people in South Korea. To this end, this study contributes to addressing the elderly suicide problem. By using seven-year panel data from the Korea Welfare Panel Survey, we extract various potential causal factors for elderly suicidal ideation based on interpersonal theory of suicide and social disorganization theory. Then a panel logit model was employed to assess the impacts of potential factors on suicidal ideation and deep learning and machine learning algorithms were used to develop a predictive model for suicidal ideation of elderly people. The results of our study provide practical implications for preventing elderly suicide by identifying causal factors of suicidal ideation and a high suicidal risk group of the elderly. This study sheds light on synergy of mixed methodology and provides various academic implications.

학생의 학업 성취를 사전에 예측하여 맞춤형 피드백과 지원을 제공하는 것은 학습 경험의 질을 향상시키고, 높은 성취를 유도하는 데 중요한 역할을 한다. 그러나 학업 성취도는 다양한 요인이 복합적으로 얽혀 있어 정확 한 예측이 어렵다. 기존 연구에서는 주로 종단연구 데이터를 활용하여 학업 성취에 영향을 미치는 변인을 탐색 해 왔으며, 최근에는 머신러닝 기법의 발전으로 다수의 변인과 비선형 관계를 동시에 분석함으로써 성취도 예측 의 정확성이 향상되고 있다. 특히, SHAP 지수를 활용한 연구를 통해 모델의 설명력을 높이고, 주요 변인의 교 육적 시사점을 시각화하는 시도가 이루어지고 있다. 본 연구는 대구교육종단연구 데이터를 활용하여 학생의 성 취도 변화를 시계열적으로 분석하고, 국어 및 수학 교과의 성취도 변화를 예측하는 주요 변인을 도출하여 학습 부진을 조기에 식별하는 데 기여하고자 한다.

Predicting students' academic achievement in advance and providing tailored support enhance learning quality and promote success. However, academic performance is influenced by complex factors, making accurate prediction challenging. While previous studies have used longitudinal data to explore key variables, recent advancements in machine learning improve accuracy by analyzing multiple factors and nonlinear relationships. In particular, SHAP-based studies enhance model interpretability and visualize key educational insights. This study uses data from the Daegu Education Longitudinal Study to analyze students’ academic performance over time and identify key predictors in Korean and mathematics, contributing to the early detection of underachievement.

Hospitals have accumulated large amounts of patient data, with each hospital’s data having unique characteristics and distributions. By leveraging this vast amount of data for machine learning, we can develop predictive models, such as those for predicting long-term outcomes in ischemic stroke patients and provide valuable information for treatment decisions. However, data privacy concerns prevent hospital data from being put together on a centralized server. This study investigates the applicability of federated learning for predicting long-term outcomes in ischemic stroke patients using data from Hallym University Sacred Heart Hospital in Pyeongchon and Hallym University Sacred Heart Hospital in Chuncheon. Patient outcomes are defined as favorable if the modified Rankin Scale (mRS) score is 0-2 and poor if the mRS score is 3-6. There are two tasks: one predicting patient outcomes at 3 months after stroke and the other predicting patient outcomes at 1 year after stroke. A simple deep neural networks model is used for implementation of the prediction model and the federated learning environment. In conclusion, the federated learning models using basic FedAVG and weighted averaging FedAVG achieved 99.4%-99.9% performance of traditional centralized learning models.

연구목적: 본 연구는 군에서 가장 많이 발생하는 교통사고의 예방을 위해 부대별로 교통사고가 발생할 확률을 사전에 예측하는 모형의 개발 방안을 제시하는 것이다. 연구방법: 이를 위해 CRISP-DM(Cross Industry Standard Process for Data Mining) 방법론을 적용하였다. CRISP-DM 프로세스는 6단계로 구성 되어 있고, 각 단계는 Waterfall Model처럼 일방향으로 구성되어 있지 않고 단계 간 피드백을 통하여 단 계별 완성도를 높이게 되어 있다. 연구결과: 전체 집단을 대상으로 기 구축된 사고조사 데이터와 동일한 데이터 세트(data set)를 구축하여 모델링한 결과 분류기준 0.5로 했을 때, 교통사고예측을 위한 모형의 정확도, 특이도, 민감도, AUC에서 의미있는 결과치를 도출하였다. 결론: 예측모형을 설계하는 과정에 서 데이터의 부족으로 인해 의미 있는 예측값을 얻기 어려운 문제점이 확인되었다. 이를 해결하기 위해 합리적 추론이 가능한 데이터 세트(data set)를 재구성 및 확대하여 데이터 부족을 해소하고, 이를 활용 한 예측모형을 설계할 수 있는 방법론을 제시하였다.

Purpose: This study proposes a method for developing a model that predicts the probability of traffic accidents in advance to prevent the most frequent traffic accidents in the military. Method: For this purpose, CRISP-DM (Cross Industry Standard Process for Data Mining) was applied in this study. The CRISP-DM process consists of 6 stages, and each stage is not unidirectional like the Waterfall Model, but improves the level of completeness through feedback between stages. Results: As a result of modeling the same data set as the previously constructed accident investigation data for the entire group, when the classification criterion was 0.5, Significant results were derived from the accuracy, specificity, sensitivity, and AUC of the model for predicting traffic accidents. Conclusion: In the process of designing the prediction model, it was confirmed that it was difficult to obtain a meaningful prediction value due to the lack of data. The methodology for designing a predictive model using the data set was proposed by reorganizing and expanding a data set capable of rational inference to solve the data shortage.

구글의 인플루엔자 의사환자(ILI) 예측 서비스 시작 이래로 웹 검색 정보를 활용한 ILI 예측 연구들이 급속도로 확산되고 있는 가운데, 본 연구는 ILI 자료와 웹 검색 정보를 활용한 한국 ILI 단기 예측 모형을 개발해 성능을 평가해 보고자 한다. 한국에 특화된 ILI 예측 모형 개발을 위해 한국질병관리본부의 ILI 감시 자료와 구글 및 네이버의 한국어 검색 정보를 ARIMA 모형과 함께 사용하였다. 모형1은 ILI 자료만 사용하였으며, 모형 2와 3은 모형1에 구글과 네이버의 검색자료를 각각 추가하였다. 모형4는 모형 2와 3의 공통 검색어를 모형1에 추가하였다. 모형 훈련기간 동안 모든 예측모형들이 95%(R2) 이상의 높은 적합도를 보였으며, 예측기간 1과 2에서 모형1이 가장 우수한 예측력(99.98%, 96.94%)을 보였다. 모형 3(a)와 4(b, c)는 전체 예측기간에서 90% 이상의 안정적인 예측력을 보였지만, 모형1의 성능에는 미치지 못하였다. 본 연구에서 정확하고 안정적인 예측력을 보인 모형들은 성능개선에 관한 보완적 연구와 더불어 국내 인플루엔자 유행 조기경보 시스템에 활용 가능하다.

Since Google launched a prediction service for influenza-like illness(ILI), studies on ILI prediction based on web search data have proliferated worldwide. In this regard, this study aims to build short-term predictive models for ILI in Korea using ILI and web search data and measure the performance of the said models. In these proposed ILI predictive models specific to Korea, ILI surveillance data of Korea CDC and Korean web search data of Google and Naver were used along with the ARIMA model. Model 1 used only ILI data. Models 2 and 3 added Google and Naver search data to the data of Model 1, respectively. Model 4 included a common query used in Models 2 and 3 in addition to the data used in Model 1. In the training period, the goodness of fit of all predictive models was higher than 95% (R2). In predictive periods 1 and 2, Model 1 yielded the best predictions (99.98% and 96.94%, respectively). Models 3(a), 4(b), and 4(c) achieved stable predictability higher than 90% in all predictive periods, but their performances were not better than that of Model 1. The proposed models that yielded accurate and stable predictions can be applied to early warning systems for the influenza pandemic in Korea, with supplementary studies on improving their performance.

Recently, the exhibition industry has developed rapidly with the development of information technologies. Most exhibitors in an exhibition plan and deploy many events that may provide advantages to visitors as a method of effective promotion. The growth and propagation of wireless technologies is a powerful marketing tool for exhibitors. However, exhibitors still rely on domain experts who are costly and time consuming because of the manual knowledge input procedure. Moreover, it is prone to biases and errors and not suitable for managing fast-growing and tremendous amounts of data that far exceed a human's ability to comprehend. To overcome these problems, data mining technology may be a great alternative, but it needs to be fit to each exhibition. This study uses data mining technology with the Predictive Model Markup Language (PMML) to suggest a system that supports intelligent services and that improves stakeholder satisfaction. This system provides advantages to the exhibitor, show organizer, and system designer, and is first enhanced by integrating data mining technologies through the knowledge of exhibition experts. Second, using the PMML, the system can automate the process of applying data mining models to solve real-time processing problems in the exhibition environment.

Purpose: Person-centered care emphasizes the therapeutic relationship between medical staff and patients, founded on mutual trust and understanding. In intensive care settings, there is growing recognition of the need to improve the care environment and promote patient-focused nursing. This study aimed to construct and validate a predictive model explaining person-centered care in intensive care units. Methods: This study employed a cross-sectional design involving 230 intensive care unit nurses working in a tertiary hospital, each with more than one year of direct patient care experience. Data were collected online between March 2 and March 30, 2023. Data analysis was conducted using IBM SPSS ver. 26.0 and AMOS ver. 25.0. Results: Statistically significant pathways were identified from nursing competency to the nursing work environment and person-centered care; from communication competence to teamwork and person-centered care; from nursing professionalism to teamwork and the nursing work environment; and from the nursing work environment to person-centered care. Nursing professionalism indirectly influenced person-centered care through teamwork and the nursing work environment. Conclusion: Enhancing person-centered care in intensive care units requires recognizing the critical roles of communication competence, nursing competency, and the nursing work environment. Developing and implementing educational programs that strengthen communication and nursing competencies, alongside initiatives that improve the nursing work environment, are essential.

Purpose: This study was conducted to assess predictor variables of husbands in multicultural families and examine the relationship among variables after setting up a hypothetical model including influencing factors, so as to provide a framework necessary for developing nursing interventions of domestic violence. Methods: The participants were 260 husbands in multicultural families in four cities in Korea. Data were analyzed using SPSS 22.0 and AMOS 20.0. Results: Self-control, social support, family of origin violence experience and stress on cultural adaptation directly affected to dysfunctional communication, and the explanatory power of the variables was 64.7%. Family of origin violence experience in domestic stress on cultural adaptation, and dysfunctional communication were directly related to domestic violence in multicultural families, and the explanatory power of the variables was 64.6%. We found out that all variables in the model had mediation effects to domestic violence through dysfunctional communication. In other words, self-control and social support had complete mediation effects, and family of origin violence experience in domestic violence and stress on cultural adaptation had partial mediation effects. Conclusions: The variables explained in this study should be considered as predictive factors of domestic violence in multicultural families, and used to provide preventive nursing intervention. Our resutls can be taken into account for developing and implementing programs on alleviating dysfunctional communication in multicultural families in Korea.

Purpose: This study investigated prediction of North Korean refugees' adaptation to the South Korean society and verified the goodness of fit between a hypothetical model and actual data in order to suggest the best model. Methods: This survey was conducted with 445 North Korean refugees living in a metropolitan area. Data were collected from September 1st to November 20th, 2012, and analyzed using SPSS Windows 18.0 and AMOS 17.0. Results: Traumatic experiences of North Korean refugees increased self-efficacy and psychological trauma. Acculturation stress decreased self-efficacy and increased passive coping. Self-efficacy affected active and passive coping, decreased psychological trauma, and increased resilience. Resilience is successful adaptation and refers to North Korean refugees' abilities to adapt effectively to stress. In particular, self-efficacy as the main parameter affecting resilience was confirmed. Conclusions: The results suggest that resilience can be improved through self-efficacy. It was the most significant factor decreasing psychological trauma and increasing resilience. Therefore, we need to develop programs for self-efficacy. The results also provide basic data for policy making for North Korean refugees.

Purpose: This study was conducted to develop and test a hypothetical model to predict health outcomes in young people with type 2 diabetes. Methods: Data were collected from 190 adults aged 23e45 with type 2 diabetes mellitus who visited the endocrinology outpatient department of the two university hospitals in South Korea from November 2, 2012 to March 7, 2013. Data collection used the structured questionnaires and patient medical records. The descriptive and correlation statistics were analyzed using PASW 18.0 and structural equation modeling procedure was performed using the AMOS 18.0 program. Results: The fit of the hypothetical model was appropriate with the ratio of the chi-square statistic to degrees of freedom at 17.00, goodness-of-fit index at .975, adjusted goodness-of-fit index at .930, root mean square error of approximation at .061, normed fit index at .926, Turker-Lewis index at .929, comparative fit index at .966. Behavioral skills were a critical factor that directly affects self-management behaviors. Through behavioral skills, motivation had a statistically significant indirect effect on selfmanagement behavior. Self-management behavior had a statistically significant direct effect on health outcome. Through self-management behavior, behavioral skills had a statistically significant indirect effect on health outcome. These variables explained 17.9% of the total variance for the health outcome in young people with type 2 diabetes. Conclusions: The results suggest that self-management behavior could be improved through nursing interventions promoting personal motivation (positive attitude), social motivation (social support), and behavioral skills (self efficacy), which can result in better health outcomes for young people with type 2 diabetes.

This paper presents methods of model predictive control (MPC) for eco-driving minimizing trip-time and energy- consumption of electric vehicles (EVs). Considering both non- convex and convex optimization problem formulations for MPC- based eco-driving, we compare the performances of nonlinear and linear MPC solutions for high-level planning of vehicle speed and charging in a driving simulation of a Munich– Cologne trip (573 km). The linear MPC can be considered as a convex quadratic programming approximation (i.e., convexified quadratic program) of the original nonlinear MPC, but its performance of optimality is shown to be comparable to the nonlinear counterparts whereas its computation speed is one order of magnitude faster.

This paper presents a model predictive control (MPC) based on a neural network (NN) model for air/fuel ration (AFR) control of automotive engines. The novelty of the paper is that the severe nonlinearity of the engine dynamics are modelled by a NN to a high precision, and adaptation of the NN model can cope with system uncertainty and time varying effects. A single dimensional optimization algorithm is used in the paper to speed up the optimization so that it can be implemented to the engine fast dynamics. Simulations on a widely used mean value engine model (MVEM) demonstrate effectiveness of the developed method.

Purpose: This study aimed to identify the key factors influencing medication adherence among patients with hypertension and diabetes using data from the 2021 Korea Health Panel Survey (KHP) and to develop a predictive model for intervention planning. Methods: Secondary analysis was conducted using data from 3,376 hypertension and 1,497 diabetes patients. SPSS and SPSS Modeler were employed for descriptive statistics and decision tree analysis with the Chi-squared Automatic Interaction Detector (CHAID) algorithm to determine the major predictors of medication adherence. Results: For hypertension patients, adherence was influenced by health literacy, outpatient visits, adverse drug reactions, number of chronic diseases, disability, and the use of mental health counseling. For diabetes patients, adherence was affected by regular healthcare provider status, perceived stress, depressive symptoms, self-rated health, and unmet medical needs. Conclusion: A decision tree model revealed that higher health literacy, frequent healthcare use, and absence of adverse drug effects improved adherence. Psychological factors such as stress and depression were also significant. These findings emphasize the need for targeted programs that enhance health literacy and address emotional factors to improve adherence among patients with chronic diseases.

Purpose: The purpose of the study was to develop a predictive model of Health-related Quality of Life (HRQoL)for Korean Goose daddies - they live alone in Korea to support their families who moved overseas for children’s education. Methods: Data were collected from 151 goose daddies from May to June of 2011 by using the structured self-reported questionnaires. The collected data were analyzed using SAS program (version 9.2) and SAS CALIS procedure. Results: Frequency of exercise, monthly income, depression, perceived physical health, and perceived mental health had direct effects on HRQoL and Depression was the variable accounting for major total effect on HRQoL. It could be explained that predictor variables accounted for 76% of the health-related quality of life. Conclusion: In order to improve Goose daddies’ HRQoL, predictive factors, such as age, exercise, nutritional status, monthly income, depression, perceived physical health, and perceived mental health, should be considered. Furthermore, should the need of the exercise and diet program, early detection of depression and the treatment for it be emphasized. Also, there is a need to establish institutional structures to support goose daddies in adversity.

Purpose: This study aimed to examine the direct and indirect effects of general characteristics, basic psychological needs, health promoting behaviors, and emotional status on sleep quality of the older adults with low back pain. Methods: We conducted a cross-sectional correlational study in B and Y cities between August and September 2020. A total of 217 older adults participated in the study and their general characteristics (age, gender, duration of back pain, pain intensity, disability, perceived health status, risk for malnutrition), basic psychological needs (autonomy, competence, relatedness), health promoting behavior (physical activity, self care), emotional status (depression, quality of life), and sleep quality were measured. Data were analyzed through descriptive analysis, independent t-test, ANOVA with Scheffe?post-hoc test, hierarchical multiple regression, and path analysis using SPSS/WIN 22.0 and AMOS 22.0. Results: The mean age of the participants was 70.31±5.39 years, the pain intensity was 6.40±1.09, and the duration of back pain was 6.69±6.46 years. The significant factors influencing sleep quality were depression (β=.45, p=.001), gender (β=-.22, p=.001), disability (β=.21, p=.003), perceived health status (β=-.21, p=.001), duration of back pain (β=-.20, p=.001), self care on back pain (β=-.15, p=.009), basic psychological needs (β=-.15, p=.001), and risk for malnutrition (β=.03, p=.028). Conclusion: The findings of this study suggest that special attention is required for older women with high levels of depression and disability due to back pain, especially those with pain duration of less than 5 years or greater than 10 years.

Purpose: This study was designed to construct a predictive model to explain quality of life of stomach cancer patients with gastrectomy. Methods: Data were collected from July 10 to August 30, 2013 through survey using self-reported questionnaires. A total of 218 patients with gastrectomy was recruited from three different hospitals. Outcome variables were exogenous ones (self efficacy and social support) and endogenous ones (depression, perceived health status, self care behavior, and quality of life). Results: Goodness-of-fit of the hypothetical model was x2=143.37, RMSEA=.07 CFI=.95, TLI=.93 SRMR=.05. Self care behavior, depression and perceived health status had significant direct effects on quality of life. Self efficacy and social support were affected quality of life indirectly. These variables explained 67.9% of total variance of quality of life, and self-care behavior was the most influential factor for quality of life. Conclusion: The findings of this study suggested that self care behavior must be considered as an intervention strategy to improve quality of life. Also a development of a specific intervention program to promote self efficacy and control depression for patients with gastrectomy is essential to facilitate their self care behaviors.

목적 본 연구의 목적은 초등학교 고학년 아동의 문제행동에 영향을 미치는 변수를 중심으로 탐색하여 영향요인들을 설명하는 모형을 구축하는 데 있다. 방법 본 연구를 위해 일개 도시의 3개 초등학교 고학년 아동과 그의 어머니를 대상으로 설문조사를 하였으며, 총 368명의 자료를 수집하였다. 수집된 자료는 SPSS 17.0 통계 프로그램을 이용하여 서술적 통계, t-test, X2-test, ANOVA를 시행하였고, 가설적 모형 검정은 AMOS 17.0 통계프로그램을 이용하였다. 결과 가설적 모형의 적합도는 GFI: .956, AGFI: .927, CFI: .953, RMSEA: .056, SRMR: .023으로 지지되었다. 모형에서 설정된 10개의 경로 중 6개의 경로가 지지되었다. 즉, 부모 양육태도에서 자아존중감으로, 부모양육태도에서 스트레스로, 부모 양육태도에서 스트레스 대처로, 부모양육태도에서 문제행동으로, 자아존중감에서 스트레스로, 자아존중감에서 문제행동으로 가는 경로가 지지되었다. 문제행동에 통계적으로 유의한 효과를 나타낸 것은 양육태도와 자아존중감이며 양육태도는 총 효과를, 자아존중감은 직접효과와 총 효과를 나타내었다. 결론 부모 양육태도와 자아존중감이 문제행동에 영향을 미치는 중요한변수인 것으로 확인되었다. 그러므로 문제행동의 발생을 줄이기 위해서는 긍정적인 양육태도를 고취시키고 아동의 자아존중감을 높이는간호중재가 이루어져야 하겠다.

Purpose: The purposes of the study were to develop and test a model which explains the relationship among factors affecting behavioral problems in elementary school children. Methods: The participants for the study were 368 elementary school children and their mothers at 3 elementary schools in one city. Data analysis was done using the SPSS 17.0 program for t-test, -test, and ANOVA and the AMOS 17.0 program for theoretical model testing. Results: The theoretical model showed a significant goodness of fit to the empirical data (Goodness of Fit Index: .96, Adjusted Goodness of Fit Index: .93 Comparative Fit Index: .95, Root Mean Square Error of Approximation: .06, Standardized Root Mean Square Residual: .02). Six paths were found to be statistically significant including from child rearing attitude to self-esteem, stress, stress coping and behavioral problems, and from self-esteem to stress and behavioral problems. Child rearing attitude showed a significant effect to behavioral problems by total effect. Self-esteem affected behavioral problems by total and direct effects. Conclusion: Child rearing attitude and selfesteem of children are important factors affecting behavioral problems in elementary school children.

본 연구는 의사결정나무분석법을 이용하여 유두혼란 방지로모유량의 분비를 증가시키고 궁극적으로 모유수유를 증진시킬수 있는 대체수유에 대하여 간호사들을 대상으로 대체수유교육요구의 예측모형을 구축한 것이다. 의사결정나무분석법은 그결과가 나무구조에 의해 모형이 표현되기 때문에 분석의 정확도 보다는 분석과정의 설명이 필요한 경우에 더 유용하며 특히어떤 예측변수가 목표변수를 설명하는데 더 중요한지를 쉽게파악할 수 있다. 본 연구 결과 대상자의 81.1%가 대체수유교육을 요구하였고,대체수유교육요구에 가장 유의한 예측변수는 대체수유교육에대한 지불의도였다. 대체수유교육에 대해 지불의도가 있는 집단의 95.2%가 대체수유교육을 요구하였고, 지불의도가 없는집단에서는 대체수유에 대해 모른다고 답한 대상자의 88.7%가대체수유교육을 요구하였으며, 대체수유에 대해 알고 있다고대답한 집단 중에서는 34.5세 이하 집단의 76.8%가 대체수유교육을 요구하는 것으로 나타났다. 대체수유에 대해 알고 있으면서 나이가 많은 집단에서는 공식적인 대체수유교육을 받은경험이 있는 집단의 88.9%가 대체수유교육을 교유하였고, 대체수유교육을 받은 경험이 없는 집단의 73.3%는 교육을 요구하지 않았다. 이러한 결과를 볼 때 대체수유에 대해 모른다고 대답한 간호사들을 중심으로 대체수유교육이 실시되어야 하겠으나, 대체수유교육을 받았다고 하더라도 최신의 정보와 함께 정확한 대체수유교육이 필요함을 알 수 있다. 대체수유교육비를 지불할의도가 있는 간호사의 대부분이 대체수유교육을 적극적으로 요구하였으나 그렇지 않은 간호사가 전체의 76.0%이기 때문에보수교육 등을 통하여 대체수유교육을 시행하는 것이 필요하겠다. 마지막으로 대체수유로 인해 신생아에게 발생하는 건강문제 등과 관련하여 논란의 여지가 있으니 이를 명확하게 규명하고 대체수유의 효과를 검증할 수 있는 연구를 제언하며, 무엇보다 대체수유에 대한 과학적인 원리와 정확한 수행방법을 간호사들에게 교육하고 대체수유 수행을 위한 간호 인력의 확보를 제언하는 바이다.

Purpose: One of the main reasons why mothers quit breast feeding is that the volume of breast milk is inadequate due to insufficiency in suckling. We believe suckling experience may be a factor affecting nipple confusion. So an alternative feeding method, namely cup, spoon, finger, or nasogastric tube feeding may be needed to prevent nipple confusion. The purpose of this study was to construct a predictive model for demand for alternative feeding education by nurses. Methods: A descriptive design with structured self-report questionnaires was used for this study. Data from 175 nurses working in hospitals in Busan were collected between April 1 and 15, 2009. Data were analyzed by decision tree method, one of the data mining techniques using SAS 9.1 and Enterprise Miner 4.3 program. Results: Of the nurses, 81.1% demanded alternative feeding education and 5 factors showed that most of them expressed intention to pay, desire to know about alternative feeding, age, and learning experience. From these results, the derived model is considered appropriative for explaining and predicting demand for alternative feeding education. Conclusion: This confirms that knowledge and compliance in alternative breast feeding for newborn babies should be correct and any inaccuracies or insufficient information should be supplemented.