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Predicting asthma patients' total cost using neural networks and linear regression
Nahmens, Isabelina Ahmad, Amani Gentimis, Thanos Ikuma, Laura
Nahmens, Isabelina
Ahmad, Amani
Gentimis, Thanos
Ikuma, Laura
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2022-12
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Article
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Keywords
Machine learning,Neural networks,Cost estimation,Predictive accuracy
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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. https://doi.org/10.62704/10057/24819
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.
Publisher
Association for Industry, Engineering and Management Systems (AIEMS)
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Series
Journal of Management & Engineering Integration
v.15 no.2
v.15 no.2
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Archival Collection
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1939-7984