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Erschienen in: Health and Technology 3/2021

24.03.2021 | Original Paper

Decision tree modeling in R software to aid clinical decision making

verfasst von: Elena G. Toth, David Gibbs, Jackie Moczygemba, Alexander McLeod

Erschienen in: Health and Technology | Ausgabe 3/2021

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Abstract

There is increasing excitement in the healthcare field about using behavioral data and healthcare analytics for disease risk prediction, clinical decision support, and overall improvement of personalized medicine. However, this excitement has not effectively translated to improved clinical outcomes due to knowledge gaps, a lack of behavioral risk models, and resistance to evidence-based practice. Reportedly, only 10–20% of clinical decisions are known to be evidence-based and this problem is further highlighted by the fact that the US spends more money on healthcare per person than any other nation, while still wrestling with poor health outcomes. Critics say there are inadequate technological resources and analytical education for clinicians to make behavioral data useful in the medical world. Healthcare technology innovators often neglect important aspects of the reality of integrating clinical data into electronic healthcare solutions. In this study, we developed a decision tree model using R statistical software to predict diabetes since it is among the top causes of death in the US, can be poorly managed, and provides an opportunity for improvement using analytics. This study examined behavioral data and healthcare analytics for use in clinical applications, demonstrating that health information professionals can develop behavioral risk factor prediction models to bridge the gap. Results indicated that decision trees are effective in classifying diabetes in an individual at up to 89.36% accuracy.

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Literatur
2.
Zurück zum Zitat National Center for Health Statistics. Health, United States, 2011: With Special Feature on Socioeconomic Status and Health. MD: Hyattsville; 2012. National Center for Health Statistics. Health, United States, 2011: With Special Feature on Socioeconomic Status and Health. MD: Hyattsville; 2012.
4.
Zurück zum Zitat Fox B. Using big data for big impact: Leveraging data and analytics provides the foundation for rethinking how to impact patient behavior. Health Manag Technol. 2011;32(11):16–16 PubMed PMID: 22141243. Fox B. Using big data for big impact: Leveraging data and analytics provides the foundation for rethinking how to impact patient behavior. Health Manag Technol. 2011;32(11):16–16 PubMed PMID: 22141243.
8.
Zurück zum Zitat Moskowitz A, McSparron J, Stone DJ, et al. Preparing a new generation of clinicians for the era of big data. Harv Med Stud Rev. 2015;2(1):24–7. Moskowitz A, McSparron J, Stone DJ, et al. Preparing a new generation of clinicians for the era of big data. Harv Med Stud Rev. 2015;2(1):24–7.
9.
Zurück zum Zitat Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System Survey (BRFSS). In: National Center for Chronic Disease Prevention and Health Promotion: Division of Population Health, (ed.). Atlanta, GA; 2016. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System Survey (BRFSS). In: National Center for Chronic Disease Prevention and Health Promotion: Division of Population Health, (ed.). Atlanta, GA; 2016.
12.
Zurück zum Zitat Monica K. Why are so few healthcare providers using EHR data analytics? EHR Intelligence xtelligent Healthcare Media, 2017. Monica K. Why are so few healthcare providers using EHR data analytics? EHR Intelligence xtelligent Healthcare Media, 2017.
13.
Zurück zum Zitat Palaniappan S and Awang R. Intelligent heart disease prediction system using data mining techniques. In: IEEE/ACS International Conference on Computer Systems and Applications Doha, Qatar; 2008, pp.108–115. IEEE. Palaniappan S and Awang R. Intelligent heart disease prediction system using data mining techniques. In: IEEE/ACS International Conference on Computer Systems and Applications Doha, Qatar; 2008, pp.108–115. IEEE.
15.
Zurück zum Zitat Kaisler S, Armour F, Espinosa JA, et al. Big data: Issues and challenges moving forward. In: Proceedings of the 46th Hawaii International Conference on System Sciences (HICSS) Maui, HI; 2013, pp.995–1004. IEEE. Kaisler S, Armour F, Espinosa JA, et al. Big data: Issues and challenges moving forward. In: Proceedings of the 46th Hawaii International Conference on System Sciences (HICSS) Maui, HI; 2013, pp.995–1004. IEEE.
16.
Zurück zum Zitat Kent J. Big data to see explosive growth, challenging healthcare organizations. Health IT Analytics. 2018. Kent J. Big data to see explosive growth, challenging healthcare organizations. Health IT Analytics. 2018.
25.
Zurück zum Zitat Sun J, Hu J, Luo D, et al. Combining knowledge and data driven insights for identifying risk factors using electronic health records. AMIA Annu Symp Proc. 2012;2012:901–10. Sun J, Hu J, Luo D, et al. Combining knowledge and data driven insights for identifying risk factors using electronic health records. AMIA Annu Symp Proc. 2012;2012:901–10.
28.
Zurück zum Zitat Bardhan I, Oh J-h, Zheng Z, et al. Predictive analytics for readmission of patients with congestive heart failure. Inf Syst Res. 2015;26(1):19–39. CrossRef Bardhan I, Oh J-h, Zheng Z, et al. Predictive analytics for readmission of patients with congestive heart failure. Inf Syst Res. 2015;26(1):19–39. CrossRef
32.
Zurück zum Zitat Steinberg GB, Church BW, McCall CJ, et al. Novel predictive models for metabolic syndrome risk: a" big data" analytic approach. Am J Manag Care. 2014;20(6):e221–8. Steinberg GB, Church BW, McCall CJ, et al. Novel predictive models for metabolic syndrome risk: a" big data" analytic approach. Am J Manag Care. 2014;20(6):e221–8.
34.
Zurück zum Zitat Dag A, Oztekin A, Yucel A, et al. Predicting heart transplantation outcomes through data analytics. Decis Support Syst. 2017;94:42–52. CrossRef Dag A, Oztekin A, Yucel A, et al. Predicting heart transplantation outcomes through data analytics. Decis Support Syst. 2017;94:42–52. CrossRef
36.
Zurück zum Zitat Turnea M, Ilea M. Predictive simulation for type II diabetes using data mining strategies applied to big data. In: The International Scientific Conference eLearning and Software for Education Bucharest, Romania, 2018, pp.481–486. Carol I National Defence University. Turnea M, Ilea M. Predictive simulation for type II diabetes using data mining strategies applied to big data. In: The International Scientific Conference eLearning and Software for Education Bucharest, Romania, 2018, pp.481–486. Carol I National Defence University.
41.
Zurück zum Zitat Bertsimas D, O’Hair A, Relyea S, et al. An analytics approach to designing combination chemotherapy regimens for cancer. Manage Sci. 2016;62(5):1511–31. CrossRef Bertsimas D, O’Hair A, Relyea S, et al. An analytics approach to designing combination chemotherapy regimens for cancer. Manage Sci. 2016;62(5):1511–31. CrossRef
45.
Zurück zum Zitat Centers for Disease Control and Prevention. Overview: BRFSS 2017. 2018. Centers for Disease Control and Prevention. Overview: BRFSS 2017. 2018.
48.
Zurück zum Zitat Centers for Disease Control and Prevention. Calculated variables in the 2017 Behavioral Risk Factor Surveillance System data file 2018. Centers for Disease Control and Prevention. Calculated variables in the 2017 Behavioral Risk Factor Surveillance System data file 2018.
49.
Zurück zum Zitat Garson GD. Missing values analysis and data imputation. Asheboro: Statistical Associates Publishing Asheboro, NC; 2015. Garson GD. Missing values analysis and data imputation. Asheboro: Statistical Associates Publishing Asheboro, NC; 2015.
50.
Zurück zum Zitat Li X and Liu B. Rule-based classification. In: Aggarwal CC (ed) Data Classification: Algorithms and Applications. Chapman & Hall/CRC; 2014, pp.121–156. Li X and Liu B. Rule-based classification. In: Aggarwal CC (ed) Data Classification: Algorithms and Applications. Chapman & Hall/CRC; 2014, pp.121–156.
51.
Zurück zum Zitat Singh NK. Prediction of breast cancer using rule based classification. Appl Med Inf. 2015;37(4):11–22. Singh NK. Prediction of breast cancer using rule based classification. Appl Med Inf. 2015;37(4):11–22.
53.
Zurück zum Zitat Society of Actuaries. The state of predictive analytics in US healthcare Modern Healthcare. 2016. Society of Actuaries. The state of predictive analytics in US healthcare Modern Healthcare. 2016.
55.
Zurück zum Zitat Partnership to Fight Chronic Disease. What is the impact of chronic disease in America? 2016. FightChronicDisease.org. Partnership to Fight Chronic Disease. What is the impact of chronic disease in America? 2016. FightChronicDisease.org.
Metadaten
Titel
Decision tree modeling in R software to aid clinical decision making
verfasst von
Elena G. Toth
David Gibbs
Jackie Moczygemba
Alexander McLeod
Publikationsdatum
24.03.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Health and Technology / Ausgabe 3/2021
Print ISSN: 2190-7188
Elektronische ISSN: 2190-7196
DOI
https://doi.org/10.1007/s12553-021-00542-w

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