Erdener Özçetin

Dr. Erdener Özçetin

orcid

Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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15 November 2023 in Neural Computing and Applications
Verified
Journal Article
Closed access
PDF file
AKAhmet Kara
Ahmet Kara
EPEngin Pekel
Engin Pekel
EÖErdener Özçetin
Erdener Özçetin
GYGazi Bilal Yıldız
Gazi Bilal Yıldız

1 September 2023 in Turkish Archives of Pediatrics
Verified
Journal Article
Open access
İDİsmail Dündar
İsmail Dündar
AAAysehan Akinci
Aysehan Akinci
EÇemine çamtosun
emine çamtosun
LKLeman Kayas
Leman Kayas
NÇNurdan Çiftci
Nurdan Çiftci
EÖErdener Özçetin
Erdener Özçetin
Type 1 diabetes mellitus,epidemiology,incidence,pediatrics,trends

19 September 2022 in Expert Systems with Applications
Verified
Journal Article
Closed access
EÖErdener Özçetin
Erdener Özçetin
GOGurkan Ozturk
Gurkan Ozturk
ZOZehra Kamisli Ozturk
Zehra Kamisli Ozturk
RKRefail Kasimbeyli
Refail Kasimbeyli
NKNergiz Kasimbeyli
Nergiz Kasimbeyli

29 December 2022 in Concurrency and Computation: Practice and Experience
Verified
Journal Article
Closed access
EÖErdener Özçetin
Erdener Özçetin
GOGurkan Ozturk
Gurkan Ozturk

16 April 2026 in International Journal of Medical Informatics
Verified
Journal Article
Closed access
EÖErdener Özçetin
Erdener Özçetin
SBSümeyye Ebrar Baş
Sümeyye Ebrar Baş
FÖFurkan Özpay
Furkan Özpay

1 January 2023 in Open Health
Verified
Journal Article
Open access
PDF file
CICenk İçÖz
Cenk İçÖz
EÖErdener Özçetin
Erdener Özçetin
In this study, we examine the spatial analysis of coronavirus disease 2019 (COVID-19) instances in Turkey. As a result, this analysis reveals that the geographic distribution of COVID-19 instances is associated with disparities in education, socioeconomic status, and population within individual provinces. By utilizing a composite indicator of development level based on provinces, we employ multivariate local Geary and multivariate local neighbor match tests to demonstrate the association between COVID-19 instances and the demographic and socioeconomic similarities or contrasts in provinces of Turkey. In addition, we provide an extremely randomized tree regression model to show how demographic and socioeconomic disparities affect COVID-19 instances. According to this model, the average household size, the proportion of the working-age population to the nonworking-age population, and the GDP per capita are the most important variables. The study’s main finding is that the important variables that were attained were also used to create an index for the degree of development in the Turkish regions. In other words, the same variables correlate with the degree of provincial development and the distribution of COVID-19 cases.