Öcal Taşar, Ceren

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Profile URL
https://gcris.yasar.edu.tr/handle/123456789/12810
Name Variants
Job Title
Dr.Öğrt.Gör.
Email Address
Main Affiliation
01. Yaşar Üniversitesi
Status
Former Staff
Website
ORCID ID
0000-0002-0652-7386
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Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
AAA-4770-2019

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Documents

5

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79

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1

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1

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0

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WoS Citation Count

12

Scopus Citation Count

15

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0

WoS Citations per Publication

12.00

Scopus Citations per Publication

15.00

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0

Supervised Theses

0

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Bio-Algorithms and Med-Systems1
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    Article
    Citation - WoS: 12
    Citation - Scopus: 15
    Use of data mining techniques to classify length of stay of emergency department patients
    (INDEX COPERNICUS INT, 2019) Gorkem Sariyer; Ceren Ocal Tasar; Gizem Ersoy Cepe; Sariyer, Görkem; Tasar, Ceren Ocal; Cepe, Gizem Ersoy; Öcal Taşar, Ceren
    Emergency departments (EDs) are the largest departments of hospitals which encounter high variety of cases as well as high level of patient volumes. Thus an efficient classification of those patients at the time of their registration is very important for the operations planning and management. Using secondary data from the ED of an urban hospital we examine the significance of factors while classifying patients according to their length of stay. Random Forest Classification and Regression Tree Logistic Regression (LR) and Multilayer Perceptron (MLP) were adopted in the data set of July 2016 and these algorithms were tested in data set of August 2016. Besides adopting and testing the algorithms on the whole data set patients in these sets were grouped into 21 based on the similarities in their diagnoses and the algorithms were also performed in these subgroups. Performances of the classifiers were evaluated based on the sensitivity specificity and accuracy. It was observed that sensitivity specificity and accuracy values of the classifiers were similar where LR and MLP had somehow higher values. In addition the average performance of the classifying patients within the subgroups outperformed the classifying based on the whole data set for each of the classifiers.