Stroke Classification Model using Logistic Regression-Reference-Cited by-同舟云学术

Stroke Classification Model using Logistic Regression

Published:2021-11-01 Issue:1 Volume:2123 Page:012016
ISSN:1742-6588
Container-title:Journal of Physics: Conference Series
language:
Short-container-title:J. Phys.: Conf. Ser.

Author:

Annas S,Aswi A,Abdy M,Poerwanto B

Abstract

Abstract This study aims to determine the factors that significantly affect the classification of stroke. The response variable used is the type of stroke, namely non-hemorrhagic stroke and hemorrhagic stroke. The predictors used were cholesterol level, blood sugar level, temperature, length of stay, pulse rate, and gender. By using logistic regression, the results obtained modeling accuracy of 74.8% where the predictors that have a significant effect (alpha <0.05) are total cholesterol and length of stay.

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Link

https://iopscience.iop.org/article/10.1088/1742-6596/2123/1/012016/pdf

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