Risk factor analysis of hypertension with logistic regression and Classification and Regression Tree (CART)

Open

J.W. Fernanda, G. Anuraga, M.A. Fahmi

2019 Journal of Physics: Conference Series Vol. 1217 Issue 1 Conference paper Cited by 2 Quartile

Abstract

Hypertension is one of the most common inherited diseases among Indonesians. This disease can affect the onset of various diseases, such as kidney failure, stroke, diabetic, and heart failure. Early detection is an effective way to control the incidence of hypertension by knowing risk factors such as age, sex, family history, genetics (irreversible/controlled risk factors), smoking habits, alcohol consumption habits, obesity, lack of physical activity that have significant effect. The methods that used to analyzed significant risk factor are logistic regression and Classification and Regression Tree (CART). This research compared the accuracy of two methods to select the best models to predict the risk of Hypertension. From the result, CART better than logistic for predict hypertension risk with AUC of 0,584. © Published under licence by IOP Publishing Ltd.

Affiliations

Department of D3 Medical Records and Health Information Management, Faculty of Health Science, Institute of Health Sciences Bhakti Wiyata Kediri, Jl. Wachid KH Hasyim 65, Bandar Lor, Kediri, East Java, 64114, Indonesia; Department of Statistics, Faculty of Science and Mathematics, PGRI Adi Buana University, Jl. Hamlet Menanggal XII, Surabaya, East Java, 60 234, Indonesia; Department of Medical Laboratory Technology D3, Faculty of Science, Technology and Analysis, Institute of Health Sciences Bhakti Wiyata Kediri, Jl. Wachid KH Hasyim 65, Kediri, East Java, 64114, Indonesia

Research at a Glance

Premium content — register to unlock

Research at a Glance

Register to unlock

Topics & SDG Alignment

Premium content — register to unlock

Topics & SDG Alignment

Register to unlock

Collaboration

Premium content — register to unlock

Collaboration

Register to unlock

Author Profile (Selected)

Premium content — register to unlock

Author Profile (Selected)

Register to unlock

References Overview

Premium content — register to unlock

References Overview

Register to unlock

Journal & Source

Premium content — register to unlock

Journal & Source

Register to unlock

Metadata & Integrity

Premium content — register to unlock

Metadata & Integrity

Register to unlock