Browsing by Author "Oner, Kevser Setenay"

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    Determination of Alzheimer's Disease Levels by Ordinal Logistic Regression and Artificial Learning Algorithms
    (Ieee, 2024) Bulut, Nurgül; Çakar, Tuna; Arslan, Ilker; Akinci, Zeynep Karaoglu; Oner, Kevser Setenay
    This study compares artificial learning algorithms and logistic regression models in determining different levels of Alzheimer's disease (AD). The research uses demographic, genetic, and neurocognitive inventory results obtained from the National Alzheimer's Coordination Center (NACC) database, along with brain volume/thickness measurements derived from MRI scanners. Deep Neural Networks, Ordinal Logistic Regression, Random Forest, Gaussian Naive Bayes, XGBoost, and LightGBM models were employed to determine the 4 different ordinal levels of AD. Although there were similarities between the accuracy rate, F1 score, AUC value, and sensitivity, specificity, and precision performance measures of each class, the highest classification rate was achieved by the Random Forest model where the oversampling was not applied. (F1 score: 0.86; accuracy: 0.86 and AUC: 0.95). The outputs of the model with the best performance were explained with the SHAP (SHapley Additive exPlanations) method. These findings indicate that non-invasive markers and artificial learning models can be used effectively in early diagnosis and decision support systems to predict different levels of Alzheimer's disease.
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    Article
    Determination of Alzheimer's Disease Stages by Artificial Learning Algorithms
    (Lifescience Global, 2025) Bulut, Nurgül; Çakar, Tuna; Arslan, İlker; Akıncı, Zeynep Karaoğlu; Oner, Kevser Setenay
    Introduction: This study aims to determine the stages of Alzheimer's disease (AD) using different machine learning algorithms, and compares the performance of these models. Methods: Demographic, genetic, and neurocognitive inventory data from the National Alzheimer's Coordinating Center (NACC) database as well as brain volume/thickness data from magnetic resonance imaging (MRI) scans were used. Deep Neural Networks, Ordinal Logistic Regression, Random Forest, Gaussian Naive Bayes, XGBoost, and LightGBM models were used to identify four different ordinal stages of AD. Results: Although the performance measures of the developed models were similar, the highest classification rate of AD stages was achieved by the Random Forest model (accuracy: 0.86; F1 score: 0.86; AUC: 0.95). The outputs of the model with the best performance were explained by the SHapley Addictive exPlanations (SHAP) method. Conclusions: This indicates that non-invasive markers and machine learning models can be used effectively in early diagnosis and decision support systems to predict stages of AD. © 2025 Elsevier B.V., All rights reserved.