Hypertension Prediction Using Machine Learning Technique

Hypertension Prediction Using Machine Learning Technique

Youngkeun Choi (Sangmyung University, South Korea) and Jae Choi (University of Texas at Dallas, USA)
Copyright: © 2020 |Pages: 11
DOI: 10.4018/IJSDS.2020070103
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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.
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Classification in medicine is one of the most important, important and widely used decision-making tools (Olivia et al., 2020; Nannia, Ghidoni & Brahnam,2020). Many modern techniques have been introduced to accurately and accurately predict hypertensions. Some tasks related to this area are described briefly as follows. The existing literature in the area of predicting hypertension within patient populations focus on several techniques such as statistical models (Echouffo-Tcheugui et al., 2013), neural networks (Poli et al., 1991; Samant, & Rao, 2013; Srivastava et al., 2013; Sumathi, & Santhakumarn, 2011; Ture et al., 2005) and fuzzy models (Abdullah et al., 2013).

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