Comparing the Performance of Regression Models, Random Forests and Neural Networks for Stroke Patients' outcome Prediction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10242762" target="_blank" >RIV/61989100:27240/19:10242762 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/8813335" target="_blank" >https://ieeexplore.ieee.org/document/8813335</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/DT.2019.8813335" target="_blank" >10.1109/DT.2019.8813335</a>

Result language

angličtina

Original language name

Comparing the Performance of Regression Models, Random Forests and Neural Networks for Stroke Patients' outcome Prediction

Original language description

The paper aimed to explore the performance of three classification methods which were used for the prediction of the clinical outcome of patients after an acute ischemic stroke. We performed the analysis with the logistic regression, the random forests algorithm and the multilayer neural networks. Each method can be evaluated with respect to its flexibility, model interpretability or prediction accuracy. In this paper, we focused on the investigation of properties of the methods with respect to our major problem, i.e. prediction of stroke patients&apos; outcome.For several years, the University Hospital Ostrava in the Czech Republic has been systematically collecting various information about patients who were transported there after suffering from an acute ischemic stroke. Therefore, efficient statistical analysis of the collected data is a vital part of the research. Naturally, the primary interest lies in the analysis of factors affecting the clinical status of patients three months after stroke which was evaluated with widely-used modified Rankin Scale. For the analysis, dichotomized clinical status was used as a target variable. By meaning, the values correspond with the values 0-3 and 4-6 of the modified Rankin Scale.All three models provided satisfactory results. The final binary logistic regression model reached the overall classification accuracy 87%, the random forests models had classification accuracy almost 90% and the multilayer neural networks almost 80% accuracy. The paper also compared the performance with other measures, e.g. well-known sensitivity, specificity or precision. (C) 2019 IEEE.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the International Conference on Information and Digital Technologies 2019, IDT 2019

ISBN

978-1-72811-401-9

ISSN

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e-ISSN

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Number of pages

8

Pages from-to

543-550

Publisher name

IEEE

Place of publication

Piscataway

Event location

Žilina, Slovakia

Event date

Jun 25, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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