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

Datos Bibliográficos

ID13775827
AutoresReinpeter Momanyi (0009-0002-9059-393X, African Population and Health Research Center, Nairobi, Kenya, autor de correspondencia), Steve Cygu (0000-0002-9284-8863, African Population and Health Research Center, Nairobi, Kenya), Agnes Kiragga (0000-0002-0969-0699, African Population and Health Research Center, Nairobi, Kenya), Henry Owoko Odero (0000-0002-3678-4332, African Population and Health Research Center, Nairobi, Kenya), Maureen Ng’etich (0009-0007-4945-4635, African Population and Health Research Center, Nairobi, Kenya), Gershim Asiki (0000-0002-9966-1153, African Population and Health Research Center, Nairobi, Kenya), Tatenda Duncan Kavu (0000-0002-9479-1143, African Population and Health Research Center, Nairobi, Kenya)
Año2025
Volumen62
Páginas469580251319905-469580251319905
Fecha de publicación2025-01-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaINQUIRY The Journal of Health Care Organization Provision and Financing (JOURNAL)
Identificadores de la revistaISSN: 0046-9580 • E-ISSN: 1945-7243
EditorialSAGE Publishing (PUBLISHER • US)
DOI10.1177/00469580251319905
PMID39995025
OpenAlexW4407930422
IdiomaEN
Citas recibidas2
Referencias citadas38

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

Association rule learning · Business · Data mining · Database · Database transaction · Environmental health · Geography · Grocery store · Kenya · Market segmentation · Purchasing · Transaction data · Computer Science · Consumer Attitudes and Food Labeling · Global Public Health Policies and Epidemiology · Marketing · Medicine · Obesity, Physical Activity, Diet

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Obras citantes distintas2
Citas por año2
Intervalo de citas2025 - 2026 (2)
Velocidad de citacióncurrent
Altamente citadoNo
Tipos de citaNeutras: 2
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