通过无监督学习技术分析肯尼亚超市的人口购物模式。

IF 2.3 4区 医学 Q3 HEALTH CARE SCIENCES & SERVICES
Reinpeter Momanyi, Steve Bicko Cygu, Agnes Kiragga, Henry Owoko Odero, Maureen Ng'etich, Gershim Asiki, Tatenda Duncan Kavu
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引用次数: 0

摘要

在肯尼亚,心血管疾病、高血压、2型糖尿病和某些癌症(肠癌、肺癌、前列腺癌和子宫癌)等非传染性疾病的发病率显著上升。这种情况并非肯尼亚独有,在非洲许多低收入和中等收入国家都很常见。许多非传染性疾病与高添加糖、高钠、高饱和脂肪和低纤维饮食有关。在非洲一些地区,关于超市顾客之间的人口差异以及他们购买健康食品和不健康食品的习惯的信息明显缺乏。这种知识差距阻碍了将杂货购买模式与非传染性疾病(包括肥胖)联系起来的能力。中低收入国家的超市通过杂货数据提供有价值的人口统计信息。本研究利用NOVA分类工具、数据挖掘和无监督机器学习技术,分析了2022年至2023年间肯尼亚5个县10家超市的杂货购买模式。利用apriori算法创建关联规则,并对关联规则进行分析,找出人口统计(地点、性别、年龄)与购买模式之间的关系。个人数据和交易数据一起被收集,因为超市记录了会员卡顾客的交易。主要目的是为公共卫生政策制定者提供指导。我们收集了3 934 122个唯一事务,每个事务都与一个使用唯一客户ID标识的客户相关联。这项研究的结果表明,从这些交易中购买的食品中,53%主要是工业加工食品,50岁以上的男性是这些食品的主要消费者。研究结果得出的结论是,这种购买趋势有可能增加老年人的非传染性疾病。因此,我们建议决策者采纳我们的建议,以保障公众健康。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Analyzing Demographic Grocery Purchase Patterns in Kenyan Supermarkets Through Unsupervised Learning Techniques.

Analyzing Demographic Grocery Purchase Patterns in Kenyan Supermarkets Through Unsupervised Learning Techniques.

Analyzing Demographic Grocery Purchase Patterns in Kenyan Supermarkets Through Unsupervised Learning Techniques.

Analyzing Demographic Grocery Purchase Patterns in Kenyan Supermarkets Through Unsupervised Learning Techniques.

Kenya is experiencing a significant increase in the prevalence of non-communicable diseases (NCDs) such as cardiovascular diseases, hypertension, Type 2 diabetes, and certain cancers (bowel, lung, prostate, and uterine). This case is not unique to Kenya but is common in many Low and Middle-Income Countries (LMICs) in Africa. Many NCDs, are linked to diets high in added sugars, sodium, saturated fat, and low in fiber. There is a notable lack of information regarding the demographic differences among supermarket customers and their purchasing habits of healthy versus unhealthy foods in some parts of Africa. This gap in knowledge hinders the ability to connect grocery purchase patterns to NCDs, including obesity. Supermarkets in LMICs offer valuable demographic insights through grocery data. This research utilizes NOVA classification tool, data mining and unsupervised machine learning techniques to analyze grocery purchase patterns in 10 supermarkets across 5 counties in Kenya between 2022 and 2023. The apriori algorithm was used to create association rules and an analysis was done on the association rules to find out the relationship between demography (location, gender, and age) with purchase patterns. Individual data was collected along with transaction data, since the supermarkets logged transactions done by loyalty card customers. The main aim is to provide guidance to policymakers in public health. We collected 3 934 122 unique transactions and each transaction was associated with a customer who was identified with a unique customer ID. Findings from this research demonstrate that 53% of food purchases from these transactions were mainly industrially processed food items and males above the age of 50 years were the main consumers of these food items. The findings lead to the conclusion that this purchase trend has a chance of rising NCDs in older people. Therefore we recommend that policymakers adopt our recommendations to safeguard public health.

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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
192
审稿时长
>12 weeks
期刊介绍: INQUIRY is a peer-reviewed open access journal whose msision is to to improve health by sharing research spanning health care, including public health, health services, and health policy.
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