Statistical And Machine Learning Modelling Of Suicıde Rate With Respect To Provinces In Turkey

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Süleyman Erdoğan Yüksek Lisans Tezi.pdf

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2022-8-18

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Erdoğan, Süleyman

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There are many factors that can affect the individual’s mental health and thus lead them to commit suicide. This study, focuses on the social and economic factors that may contribute to the suicide rates in all provinces of Turkey. Possible effects of these factors are studied in an 8 year period between 2012-2019 using standard longitudinal data modelling methods and hybrid modelling methods. Standard longitudinal data models include; fixed effect models, random effect models and transition models whereas hybrid models include Mixed Effect Regression Tree(MERT) models, Mixed Effect Random Forest(MERF) models, Random Effect - Expectation Maximization Trees(RE-EM Tree) models. The overall results suggest that the divorce and health- care accessibility of the provinces have significant relation with the suicide rates of provinces. Another observed result was the hybrid models overall performed better than the standard longitudinal data models.

Subject Keywords

Longitudinal Data, Fixed Effect Models, Random Effects Models, Transition Models, Hybrid Models

URI

https://hdl.handle.net/11511/99722

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

S. Erdoğan, “Statistical And Machine Learning Modelling Of Suicıde Rate With Respect To Provinces In Turkey,” M.S. - Master of Science, Middle East Technical University, 2022.