Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Predictive Models for Alzheimer's Disease Diagnosis and Mci Identification: The Use of Cognitive Scores and Artificial Intelligence Algorithms; [Modeles Predictifs Pour Le Diagnostic De La Maladie D'alzheimer Et L'identification De La Deterioration Cognitive Legere: Exploitation Des Scores Cognitifs Et Des Algorithmes D'intelligence Artificielle] Publisher



Sadeghzadeh SA1 ; Nazari MJ2 ; Aljamaeen M1 ; Yazdani FS3 ; Mousavi SY4 ; Vahabi Z5
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Computing, Staffordshire University, Stoke-on-Trent, ST4 2DE, United Kingdom
  2. 2. Computer Science and Mathematics Department, Amirkabir University of Technology, 424 Hafez Ave, Tehran, Iran
  3. 3. Faculty of Education Sciences and Psychology, Ferdowsi University of Mashhad, Mashhad, Iran
  4. 4. Department of Psychiatry, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. Department of Geriatrics Medicine and Cognitive Neurology and Neuropsychiatry Department, Tehran University of Medical Sciences, Tehran, Iran

Source: NPG Neurologie - Psychiatrie - Geriatrie Published:2024


Abstract

The paper presents a comprehensive study on predictive models for Alzheimer's disease (AD) and mild cognitive impairment (MCI) diagnosis, implementing a combination of cognitive scores and artificial intelligence algorithms. The research includes detailed analyses of clinical and demographic variables such as age, education, and various cognitive and functional scores, investigating their distributions and correlations with AD and MCI. The study utilizes several machine-learning classifiers, comparing their performance through metrics like accuracy, precision, and area under the ROC curve (AUC). Key findings include the influence of gender on AD prevalence, the potential protective effect of education, and the significance of functional decline and cognitive performance scores in the models. The results demonstrate the effectiveness of ensemble methods and the robustness of the models across different data subsets, highlighting the potential of artificial intelligence in enhancing diagnostic accuracy for Alzheimer's disease and mild cognitive impairment. © 2024 The Author(s); Cette etude explore l'application des algorithmes d'apprentissage automatique pour le diagnostic de la maladie d'Alzheimer (MA) et l'identification de la deterioration cognitive legere (DCL), en utilisant des scores cognitifs parmi d'autres variables cliniques et demographiques. Nous decrivons notre methodologie, incluant la collecte de donnees, le pretraitement, la selection des caracteristiques, et l'utilisation de divers classificateurs d'apprentissage machine. Les resultats mettent en evidence l'efficacite des methodes d'ensemble dans la prediction de la MA et de la DCL, discutent des implications de ces resultats pour le diagnostic precoce et l'intervention, et suggerent des directions pour les recherches futures. © 2024 The Author(s)
Related Docs
Experts (# of related papers)