Stroke Classification Model using Logistic Regression

Annas, Suwardi and Aswi, Aswi and Abdy, Muhammad and Poerwanto, Bobby (2021) Stroke Classification Model using Logistic Regression. Journal of Physics: Conference Series, 2123. ISSN 1742-6596

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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.

Item Type:	Article
Subjects:	FMIPA > STATISTIKA - (S1) KARYA ILMIAH DOSEN Universitas Negeri Makassar > KARYA ILMIAH DOSEN
Divisions:	KOLEKSI KARYA ILMIAH UPT PERPUSTAKAAN UNM MENURUT FAKULTAS > KARYA ILMIAH DOSEN KARYA ILMIAH DOSEN
Depositing User:	Dr. Aswi Aswi
Date Deposited:	17 Jul 2022 06:12
Last Modified:	28 Nov 2022 03:14
URI:	http://eprints.unm.ac.id/id/eprint/23858

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