Abstract
As the health care system moves toward value-based care, Clinical Management System (CMS) has designed a number of programs to improve the quality of patient care. One of these programs is called the Hospital Patient Admission Cost Analysis Program, which helps the patient and the hospital to diagnose the disease and estimate the cost of hospitalization. According to the World Health Organization (WHO), the personal and medical costs have skyrocketed faster than the global economy. Major attributes which cause an increase in expenditure include smoking, ageing and increased Body Mass Index (BMI). In this study, we find a correlation between medical costs and various items using the insurance data of different people with characteristics such as smoking, age, the number of children, region and BMI. This study can also be used to demonstrate different models of regression that can be used to forecast insurance costs. Machine learning significantly reduces human efforts because machine learning models can compute cost calculations in short time, for which human beings take much more time.
This is a preview of subscription content, log in via an institution to check access.
References
S. Chatterjee, C. Levin, R. Laxminarayan, "Unit cost of medical services at different hospitals in India," PLoS ONE, v.8, n.7, p.e69728 (2013). DOI: https://doi.org/10.1371/journal.pone.0069728.
A. Gelman, J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge University Press, 2006). DOI: https://doi.org/10.1017/CBO9780511790942.
D. Gefen, D. Straub, M.-C. Boudreau, "Structural equation modeling and regression: guidelines for research practice," Commun. Assoc. Inf. Syst., v.4, n.1, p.7 (2000). DOI: https://doi.org/10.17705/1CAIS.00407.
P. Fenn, "Current cost of medical negligence in NHS hospitals: analysis of claims database," BMJ, v.320, n.7249, p.1567 (2000). DOI: https://doi.org/10.1136/bmj.320.7249.1567.
R. Hillestad, J. Bigelow, A. Bower, F. Girosi, R. Meili, R. Scoville, R. Taylor, "Can electronic medical record systems transform health care? Potential health benefits, savings, and costs," Heal. Aff., v.24, n.5, p.1103 (2005). DOI: https://doi.org/10.1377/hlthaff.24.5.1103.
M. Hanafy, O. M. A. Mahmoud, "Predict health insurance cost by using machine learning and DNN regression models," Int. J. Innov. Technol. Explor. Eng., v.10, n.3, p.137 (2021). DOI: https://doi.org/10.35940/ijitee.C8364.0110321.
S. Kapsiani, B. J. Howlin, "Random forest classification for predicting lifespan-extending chemical compounds," Sci. Reports, v.11, n.1, p.13812 (2021). DOI: https://doi.org/10.1038/s41598-021-93070-6.
J. W. Robinson, "Regression tree boosting to adjust health care cost predictions for diagnostic mix," Heal. Serv. Res., v.43, n.2, p.755 (2008). DOI: https://doi.org/10.1111/j.1475-6773.2007.00761.x.
R. Sturm, "The effects of obesity, smoking, and drinking on medical problems and costs," Heal. Aff., v.21, n.2, p.245 (2002). DOI: https://doi.org/10.1377/hlthaff.21.2.245.
J. Cawley, A. Biener, C. Meyerhoefer, Y. Ding, T. Zvenyach, B. G. Smolarz, A. Ramasamy, "Direct medical costs of obesity in the United States and the most populous states," J. Manag. Care Spec. Pharm., v.27, n.3, p.354 (2021). DOI: https://doi.org/10.18553/jmcp.2021.20410.
P. Schober, T. R. Vetter, "Linear regression in medical research," Anesth. Analg., v.132, n.1, p.108 (2021). DOI: https://doi.org/10.1213/ANE.0000000000005206.
Kaggle Inc., Medical Cost Personal Datasets. URI: https://www.kaggle.com/mirichoi0218/insurance.
L. Breiman, "Random forests," Mach. Learn., v.45, p.5 (2001). DOI: https://doi.org/10.1023/A:1010933404324.
J. Hatwell, M. M. Gaber, R. M. A. Azad, "CHIRPS: Explaining random forest classification," Artif. Intell. Rev., v.53, n.8, p.5747 (2020). DOI: https://doi.org/10.1007/s10462-020-09833-6.
M. Schonlau, R. Y. Zou, "The random forest algorithm for statistical learning," Stata J. Promot. Commun. Stat. Stata, v.20, n.1, p.3 (2020). DOI: https://doi.org/10.1177/1536867X20909688.
L. de la Perrelle, G. Radisic, M. Cations, B. Kaambwa, G. Barbery, K. Laver, "Costs and economic evaluations of quality improvement collaboratives in healthcare: a systematic review," BMC Heal. Serv. Res., v.20, n.1, p.155 (2020). DOI: https://doi.org/10.1186/s12913-020-4981-5.
P. Chandrakar, Github Link. URI: https://github.com/PreetiChandrakar/predicting-hospital-charges.
B. Abdelmoula, M. Torjmen, N. B. Abdelmoula, "Machine learning based prediction tool of hospitalization cost," in 2021 22nd International Arab Conference on Information Technology (ACIT) (IEEE, 2021). DOI: https://doi.org/10.1109/ACIT53391.2021.9677110.
B. Langenberger, T. Schulte, O. Groene, "The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data," PLOS ONE, v.18, n.1, p.e0279540 (2023). DOI: https://doi.org/10.1371/journal.pone.0279540.
Z. Xiao, X. Xu, H. Xing, F. Song, X. Wang, B. Zhao, "A federated learning system with enhanced feature extraction for human activity recognition," Knowledge-Based Syst., v.229, p.107338 (2021). DOI: https://doi.org/10.1016/j.knosys.2021.107338.
Acknowledgments
The author would like to thank Information Technology department, Shri Shankaracharya Technical Campus, Bhilai (CG), India for providing facilities to carry out this work. We would also like to thank the editor and the anonymous reviewers for their valuable suggestions.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
ADDITIONAL INFORMATION
D. Shukla, P. Chandrakar
The authors declare that they have no conflicts of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
The initial version of this paper in Ukrainian is published in the journal “Izvestiya Vysshikh Uchebnykh Zavedenii. Radioelektronika,” ISSN 2307-6011 (Online), ISSN 0021-3470 (Print) on the link http://radio.kpi.ua/article/view/S0021347023010016 with DOI: https://doi.org/10.20535/S0021347023010016
Additional information
Translated from Izvestiya Vysshikh Uchebnykh Zavedenii. Radioelektronika, No. 12, pp. 778-789, December, 2022 https://doi.org/10.20535/S0021347023010016 .
About this article
Cite this article
Shukla, D., Chandrakar, P. Predicting Patient Hospital Charges Using Machine Learning. Radioelectron.Commun.Syst. 65, 665–673 (2022). https://doi.org/10.3103/S0735272723010016
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S0735272723010016