Stroke Classification Model using Logistic Regression

S. Annas; A. Aswi; M. Abdy; B. Poerwanto

doi:10.1088/1742-6596/2123/1/012016

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Stroke Classification Model using Logistic Regression

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S. Annas, A. Aswi, M. Abdy, B. Poerwanto

2021 Journal of Physics: Conference Series Vol. 2123 Issue 1 Conference paper Cited by 12 SDG 3

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 cholesterol level and length of stay. © 2021 Institute of Physics Publishing. All rights reserved.

Affiliations

Statistics Department, Universitas Negeri Makassar, Indonesia; Mathematics Department, Universitas Negeri Makassar, Indonesia

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