Customer 360-degree Insights in Predicting Chronic Diabetes

• URL: http://arxiv.org/abs/2109.01863v1
• Date: Sat, 4 Sep 2021 13:12:53 GMT
• Title: Customer 360-degree Insights in Predicting Chronic Diabetes
• Authors: Asish Satpathy, Satyajit Behari
• Abstract summary: We have mined a sample of ten million customers' 360-degree data representing the state of Texas, USA. We have developed a classification model to predict chronic diabetes with an accuracy of 80%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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