 Machine learning prediction of chronic diabetes based on a person's demography and lifestyle informationAsish Satpathy and Satyajit Behari International Journal of Data Science, 2022, vol. 7, issue 3, 210-228 Abstract: Chronic diseases such as diabetes are prevalent globally and responsible for many deaths yearly. In addition, treatments for such chronic diseases account for a high healthcare cost. However, research has shown that diabetes can be proactively managed and prevented while lowering healthcare costs. We have mined a sample of ten million customers' 360° insight that includes behavioural, demographic, and lifestyle information, representing the state of Texas, USA, with attributes current as of late 2018. The sample, obtained from a market research data vendor, has over 1000 customer attributes consisting of behavioural, demographic, lifestyle, and, in some cases, self-reported chronic conditions such as diabetes or hypertension. In this study, we have developed a classification model to predict chronic diabetes with an accuracy of 80%. In addition, we demonstrate a use case where a large volume of customers' 360° data can be helpful to predict and hence proactively prevent and manage a person's chronic diabetes. Customer and person are both used interchangeably throughout the paper. Keywords: data mining in health care; classification analysis with lifestyle and demographic data; customers' 360° insights; data mining for predicting diabetes. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdsci:v:7:y:2022:i:3:p:210-228 Access Statistics for this article More articles in International Journal of Data Science from Inderscience Enterprises Ltd |
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