라이프스타일이 빠르게 변화하고 있고, 식생활과 식품가공 기술의 발전에 따라 가구별로 식품 소비패턴이 매우 다양하다. 본 논문은 가구 단위의 농식품 구매 정보를 담고 있는 농촌진흥청이 구축하고 있는 소비자 패널 데이터의 식품군을 재분류하고 농식품 소비행위 주체인 패널 대표자의 연령대별로 그룹화하여 농식품 소비 특성 비교를 하였다. 연령대 구분의 기준은 대사질환 유병률로 20% 이상인 60대 이상 그룹과 10% 미만인 30~40대 그룹으로 나누었다. LightGBM 알고리즘을 사용하여 30~40대와 60대 이상의 식품 소비패턴의 차이를 분류 분석한 결과 정밀도는 0.85, 재현율은 0.71, F1_score는 0.77로 나타났다. 변수중요도의 결과는 과자류, 엽경채나물류, 조미채류, 과채류, 수산물류 순이었으며, SHAP 지표의 상위 5개 값은 과자류, 수산물류, 조미채류, 과채류, 엽경채나물류 순이었다. 이상치에 민감한 평균을 대신한 중앙값으로 소비패턴을 이진 분류한 결과 과자류의 경우 30~40대가 60대보다 두 배 이상 높은 것을 알 수 있었다. 이외의 변수에서도 30~40대와 60대 이상 사이에서 유의미한 차이를 보였다. 연구 결과 30~40대는 60대보다 과자류를 두 배 이상 소비하는 패턴을 보였으며, 60대의 경우 30~40대보다 수산물, 조미채류, 과채류, 엽경채나물류를 두 배 이상 섭취하였다. 상위 5개 품목 외에도 밀가공식품인 과자, 빵류, 면류에서 30~40대의 소비가 높았으며, 이는 60대의 식품 소비패턴과 차이를 보였다.
라이프스타일이 빠르게 변화하고 있고, 식생활과 식품가공 기술의 발전에 따라 가구별로 식품 소비패턴이 매우 다양하다. 본 논문은 가구 단위의 농식품 구매 정보를 담고 있는 농촌진흥청이 구축하고 있는 소비자 패널 데이터의 식품군을 재분류하고 농식품 소비행위 주체인 패널 대표자의 연령대별로 그룹화하여 농식품 소비 특성 비교를 하였다. 연령대 구분의 기준은 대사질환 유병률로 20% 이상인 60대 이상 그룹과 10% 미만인 30~40대 그룹으로 나누었다. LightGBM 알고리즘을 사용하여 30~40대와 60대 이상의 식품 소비패턴의 차이를 분류 분석한 결과 정밀도는 0.85, 재현율은 0.71, F1_score는 0.77로 나타났다. 변수중요도의 결과는 과자류, 엽경채나물류, 조미채류, 과채류, 수산물류 순이었으며, SHAP 지표의 상위 5개 값은 과자류, 수산물류, 조미채류, 과채류, 엽경채나물류 순이었다. 이상치에 민감한 평균을 대신한 중앙값으로 소비패턴을 이진 분류한 결과 과자류의 경우 30~40대가 60대보다 두 배 이상 높은 것을 알 수 있었다. 이외의 변수에서도 30~40대와 60대 이상 사이에서 유의미한 차이를 보였다. 연구 결과 30~40대는 60대보다 과자류를 두 배 이상 소비하는 패턴을 보였으며, 60대의 경우 30~40대보다 수산물, 조미채류, 과채류, 엽경채나물류를 두 배 이상 섭취하였다. 상위 5개 품목 외에도 밀가공식품인 과자, 빵류, 면류에서 30~40대의 소비가 높았으며, 이는 60대의 식품 소비패턴과 차이를 보였다.
Lifestyle is changing rapidly, and food consumption patterns vary widely among households as dietary and food processing technologies evolve. This paper reclassified the food group of consumer panel data established by the Rural Development Administration, which contains information on purchasing ag...
Lifestyle is changing rapidly, and food consumption patterns vary widely among households as dietary and food processing technologies evolve. This paper reclassified the food group of consumer panel data established by the Rural Development Administration, which contains information on purchasing agricultural products by household unit, and compared the consumption characteristics of agricultural products by age group. The criteria for age classification were divided into groups in their 60s and older with a prevalence of 20% or more metabolic diseases and groups in their 30s and 40s with less than 10%. Using the LightGBM algorithm, we classified the differences in food consumption patterns in their 30s and 50s and 60s and found that the precision was 0.85, the reproducibility was 0.71, and F1_score was 0.77. The results of variable importance were confectionery, folio, seasoned vegetables, fruit vegetables, and marine products, followed by the top five values of the SHAP indicator: confectionery, marine products, seasoned vegetables, fruit vegetables, and folio vegetables. As a result of binary classification of consumption patterns as a median instead of the average sensitive to outliers, confectionery showed that those in their 30s and 40s were more than twice as high as those in their 60s. Other variables also showed significant differences between those in their 30s and 40s and those in their 60s and older. According to the study, people in their 30s and 40s consumed more than twice as much confectionery as those in their 60s, while those in their 60s consumed more than twice as much marine products, seasoned vegetables, fruit vegetables, and folioce or logistics as much as those in their 30s and 40s. In addition to the top five items, consumption of 30s and 40s in wheat-processed snacks, breads and noodles was high, which differed from food consumption patterns in their 60s.
Lifestyle is changing rapidly, and food consumption patterns vary widely among households as dietary and food processing technologies evolve. This paper reclassified the food group of consumer panel data established by the Rural Development Administration, which contains information on purchasing agricultural products by household unit, and compared the consumption characteristics of agricultural products by age group. The criteria for age classification were divided into groups in their 60s and older with a prevalence of 20% or more metabolic diseases and groups in their 30s and 40s with less than 10%. Using the LightGBM algorithm, we classified the differences in food consumption patterns in their 30s and 50s and 60s and found that the precision was 0.85, the reproducibility was 0.71, and F1_score was 0.77. The results of variable importance were confectionery, folio, seasoned vegetables, fruit vegetables, and marine products, followed by the top five values of the SHAP indicator: confectionery, marine products, seasoned vegetables, fruit vegetables, and folio vegetables. As a result of binary classification of consumption patterns as a median instead of the average sensitive to outliers, confectionery showed that those in their 30s and 40s were more than twice as high as those in their 60s. Other variables also showed significant differences between those in their 30s and 40s and those in their 60s and older. According to the study, people in their 30s and 40s consumed more than twice as much confectionery as those in their 60s, while those in their 60s consumed more than twice as much marine products, seasoned vegetables, fruit vegetables, and folioce or logistics as much as those in their 30s and 40s. In addition to the top five items, consumption of 30s and 40s in wheat-processed snacks, breads and noodles was high, which differed from food consumption patterns in their 60s.
Anand, S. S., Hawkes, C., De Souza, R. J., Mente, A., Dehghan, M., Nugent, R., Zulyniak, M. A., Weis, T., Bernstein, A. M., Krauss, R. M., Kromhout, D., Jenkins, D. J. A., Malik, V., Martinez-Gonzalez, M. A., Mozaffarian, D., Yusuf, S., Willett, W. C., & Popkin, B. M. (2015). Food Consumption and its Impact on Cardiovascular Disease: Importance of Solutions Focused on the Globalized Food System A Report from the Workshop Convened by the World Heart Federation. In Journal of the American College of Cardiology (Vol. 66, Issue 14, pp. 1590-1614). Elsevier USA. https://doi.org/10.1016/j.jacc.2015.07.050
Chun, N., & Chae, H. J. (2017). Prevalence of Metabolic Syndrome and Its Components in Adult Women. Journal of Korean Biological Nursing Science, 20(4), 261-269. https://doi.org/10.7586/jkbns.2018.20.4.261
Microsoft Corporation. LightGBM Release 3.2.1.99. 2021. https://lightgbm.readthedocs.io/
Al Daoud, E. (2019). Comparison between XGBoost, LightGBM and CatBoost using a home credit dataset. International Journal of Computer and Information Engineering, 13(1), 6-10.
한성희. (2010). 기혼여성의 가정생활관리행동: 웰빙지향 식품 구매행동 및 식생활 행동과 소비만족도, 14(2), 127-152.
AI-Helper
※ AI-Helper는 부적절한 답변을 할 수 있습니다.