Determination of Alzheimer's Disease Stages by Artificial Learning Algorithms

Authors: Bulut, Nurgül, Çakar, Tuna, Arslan, İlker, Akıncı, Zeynep Karaoğlu, Oner, Kevser Setenay

Source: International Journal of Statistics in Medical Research. 14:532-542

Subject Terms: Matplotlib 3.6.0, Adult, Explainable Artificial Intelligence, Scikit-Learn 1.1.3, Major Clinical Study, Neuropsychiatric Inventory, Deep Neural Network, Python Programming Language Version 3.10, Learning Algorithm, Alzheimer's Disease, Sensitivity and Specificity, Article, Machine Learning, Python Libraries Numpy 1.23.0, Artificial Intelligence, Alzheimer Disease, Controlled Study, Neuropathology, Artificial Learning Algorithm, Aged, Principal Component Analysis, Tensorflow 2.10, Nuclear Magnetic Resonance Imaging, Random Forest, Receiver Operating Characteristic, National Alzheimer's Coordinating Center, Depression, Bayesian Learning, Geriatric Depression Scale, Machine Learning Algorithm, Mini Mental State Examination, Decision Support System, Shapley Addictive Explanations, Statsmodel 0.13.5, Logistic Regression Analysis, Montreal Cognitive Assessment, Physician, Pandas 1.5.0, Diagnostic Test Accuracy Study, Cognitive Defect, XGBoost, Human

Access URL: https://hdl.handle.net/20.500.11779/3094
https://doi.org/10.6000/1929-6029.2025.14.50