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Computer Science > Machine Learning

arXiv:2109.01863 (cs)
[Submitted on 4 Sep 2021]

Title:Customer 360-degree Insights in Predicting Chronic Diabetes

Authors:Asish Satpathy, Satyajit Behari
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Abstract:Chronic diseases such as diabetes are quite prevalent in the world and are responsible for a significant number of deaths per year. 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 these healthcare costs. We have mined a sample of ten million customers' 360-degree data representing the state of Texas, USA, with attributes current as of late 2018. The sample received from a market research data vendor has over 1000 customer attributes consisting of demography, lifestyle, and in some cases self-reported chronic conditions. In this study, we have developed a classification model to predict chronic diabetes with an accuracy of 80%. We demonstrate a use case where a large volume of 360-degree customer data can be useful to predict and hence proactively prevent chronic diseases such as diabetes.
Comments: Submitted to journal for publication
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2109.01863 [cs.LG]
  (or arXiv:2109.01863v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.01863

Submission history

From: Asish Satpathy [view email]
[v1] Sat, 4 Sep 2021 13:12:53 UTC (990 KB)
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