Predicting asthma patients' total cost using neural networks and linear regression

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Issue Date
2022-12
Embargo End Date
Authors
Nahmens, Isabelina
Ahmad, Amani
Gentimis, Thanos
Ikuma, Laura
Advisor
Citation

Nahmens, I., Ahmad, A., Gentimis, T., Ikuma, L. (2022). Predicting asthma patients' total cost using neural networks and linear regression. Journal of Management & Engineering Integration, 15(2), 1-9.

Abstract

In this paper, we analyze a population of asthma patients trying to predict the total cost of their treatment based on various demographic, clinical, and pharmacological data. We are comparing a neural network architecture with a simple linear regression using data from a healthcare insurance provider based in Louisiana. Our first focus was to explore the factors associated with the total cost of asthma treatment. Then we identified a sufficient threshold of data for which the neural networks outperform the linear regression models in terms of predictive accuracy. We showed that even with a simple Neural Network architecture, after approximately 6,000 randomly selected data points Neural Networks outperform Linear Regression almost always.

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Published in SOAR: Shocker Open Access Repository by Wichita State University Libraries Technical Services, November 2022.
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