Hypertension Prediction Using Machine Learning Technique

Youngkeun Choi and Jae Choi
Additional contact information
Youngkeun Choi: Sangmyung University, South Korea
Jae Choi: University of Texas at Dallas, USA

International Journal of Strategic Decision Sciences (IJSDS), 2020, vol. 11, issue 3, 52-62

Abstract: Machine learning technology is used in advanced data analysis and optimization approaches for different kinds of medical problems. Hypertension is complicated, and every year it causes a lot of many severe illnesses such as stroke and heart disease. This study essentially had two primary goals. Firstly, this paper intends to understand the role of variables in hypertension modeling better. Secondly, the study seeks to evaluate the predictive performance of the decision trees. Based on these results, first, age, BMI, and average glucose level influence hypertension significantly, while other variables have an influence. Second, for the full model, the accuracy rate is 0.905, which implies that the error rate is 0.095. Among the patients who were predicted not to have hypertension, the accuracy that would not have hypertension was 90.51%, and the accuracy that had strike was 30.77% among the patients who were predicted to have hypertension.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJSDS.2020070103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jsds00:v:11:y:2020:i:3:p:52-62

Access Statistics for this article

International Journal of Strategic Decision Sciences (IJSDS) is currently edited by Saeed Tabar

More articles in International Journal of Strategic Decision Sciences (IJSDS) from IGI Global
Bibliographic data for series maintained by Journal Editor ().