Customer 360-degree Insights in Predicting Chronic Diabetes

TL;DR

This paper presents a classification model using 360-degree customer data to predict chronic diabetes with 80% accuracy, demonstrating how comprehensive customer insights can aid in proactive disease prevention and healthcare cost reduction.

Contribution

The study introduces a novel approach leveraging extensive customer data for predicting chronic diabetes, highlighting the potential of 360-degree insights in healthcare analytics.

Findings

Achieved 80% accuracy in predicting chronic diabetes.

Utilized over 1000 customer attributes including demography and lifestyle.

Showcased a practical use case for proactive disease prevention.

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.