Tez Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Fen Edebiyat Fakültesi, İstatistik Bölümü, Türkiye
Tez Danışmanı: Özlem Ilk
Tezin Onay Tarihi: 2023
Tezin Dili: İngilizce
Özet:
Alzheimer’s disease (AD) is a significant global health issue that affects both
individuals and society for older adults. The symptoms of the disease can be
observed over time, making the structure longitudinal. Classical statistical models
and machine learning algorithms can be used to analyze these datasets. This study
consists of two parts: First, a real dataset is used to find the features affecting
dementia status and compare the performances of models. Secondly, a simulation
study based on the real dataset with a different number of subjects and an equal
number of time points for each subject is conducted to apply and compare the model
performances. The classical mixed models, their extended versions, and hybrid
models, Boruta, GEE, GLMM, HGLM, GLMMLasso, GPBoost, GLMMTree, and
HRF are used for both parts. As a result, GPBoost learns and classifies the dementia
status well but overfits due to the small sample size in the dataset, and tree-based
algorithms are efficient in predicting the dementia status when a new subject enters
the study for the real dataset. For the simulation study, all methods have similar
results, but HGLM, GPBoost and GLMMLasso algorithms have better performances
regardless of the sample size and balance of the dataset.
Keywords: Longitudinal Data Analysis, Alzheimer’s Disease Prediction, Machine
Learning Models, Mixed Effects Models