research-article

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease: A Review

Authors:

C. T. LinAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 16, Issue 1s

Article No.: 30, Pages 1 - 35

https://doi.org/10.1145/3344998

Published: 17 April 2020 Publication History

Abstract

Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.

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Mathews EJabez J(2024)Classifying alzheimer's disease from sMRI data using a hybrid deep learning approachesSalud, Ciencia y Tecnología - Serie de Conferencias10.56294/sctconf20246513(651)Online publication date: 7-Mar-2024
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Index Terms

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease: A Review
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods
    2. Machine learning approaches
      1. Neural networks
  2. Parallel computing methodologies
    1. Parallel algorithms
      1. Vector / streaming algorithms

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Early Diagnosis of Alzheimer's Disease using Machine Learning Based Methods
IC3-2021: Proceedings of the 2021 Thirteenth International Conference on Contemporary Computing

Alzheimer's Disease is a gradual, irreversible brain disease that deteriorates a patient's memory, cognitive functions and shrinks the brain's size, eventually leading to death. Based on recent research, it is found that AD is the third leading cause of ...
On the detection of Alzheimer’s disease using fuzzy logic based majority voter classifier
Abstract
Alzheimer’s disease (AD) is considered to be one of the most frequent neurogenerative dementia in the elderly population. In order to improve the quality of life span, early detection of AD has drawn significant attention to the researchers ...
Multi-modal discriminative dictionary learning for Alzheimer's disease and mild cognitive impairment

A multi-modal discriminant DL algorithm for AD/MCI classification is proposed.The mSCDDL could enhance the recognition rate compared with some other algorithms.A weighted combination framework for multi-feature fusion is adopted into the DL scheme. ...

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 16, Issue 1s

Special Issue on Multimodal Machine Learning for Human Behavior Analysis and Special Issue on Computational Intelligence for Biomedical Data and Imaging

January 2020

376 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3388236

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 April 2020

Accepted: 01 July 2019

Revised: 01 July 2019

Received: 01 May 2019

Published in TOMM Volume 16, Issue 1s

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Council of Scientific 8 Industrial Research (CSIR), New Delhi, INDIA under Extra Mural Research (EMR) Scheme
Department of Science and Technology, INDIA as Ramanujan fellowship

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Al-Rawashdeh HUsman ADutta AWahab Sait A(2024)Ensemble Learning-based Alzheimer’s Disease Diagnosis Using Magnetic Resonance ImagingJournal of Disability Research10.57197/JDR-2024-00673:6Online publication date: 2024
https://doi.org/10.57197/JDR-2024-0067
Mathews EJabez J(2024)Classifying alzheimer's disease from sMRI data using a hybrid deep learning approachesSalud, Ciencia y Tecnología - Serie de Conferencias10.56294/sctconf20246513(651)Online publication date: 7-Mar-2024
https://doi.org/10.56294/sctconf2024651
Balcıoğlu YCubukcu Cerası CHuseynov FKılıç A(2024)Sosyal Medyanın Tüketicilerin Yeşil Tüketim Algısı Üzerindeki Etkisini Anlamak için Kapsamlı bir Metin Madenciliği UygulamasıBilgisayar Bilimleri ve Mühendisliği Dergisi10.54525/bbmd.145442217:1(28-37)Online publication date: 18-Mar-2024
https://doi.org/10.54525/bbmd.1454422
Karim SFahad MRathore R(2024)Identifying discriminative features of brain network for prediction of Alzheimer’s disease using graph theory and machine learningFrontiers in Neuroinformatics10.3389/fninf.2024.138472018Online publication date: 18-Jun-2024
https://doi.org/10.3389/fninf.2024.1384720
Yaqoob NKhan MMasood SAlbarakati HHamza AAlhayan FJamel LMasood A(2024)Prediction of Alzheimer's disease stages based on ResNet-Self-attention architecture with Bayesian optimization and best features selectionFrontiers in Computational Neuroscience10.3389/fncom.2024.139384918Online publication date: 25-Apr-2024
https://doi.org/10.3389/fncom.2024.1393849
Javeed AAnderberg PGhazi ANoor AElmståhl SBerglund J(2024)Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementiaFrontiers in Bioengineering and Biotechnology10.3389/fbioe.2023.133625511Online publication date: 8-Jan-2024
https://doi.org/10.3389/fbioe.2023.1336255
Gupta SParikh JJain RKashi NKhurana PMehta JHemanth J(2024)Dementia detection using parameter optimization for multimodal datasetsIntelligent Decision Technologies10.3233/IDT-23053218:1(343-369)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/IDT-230532
Öcal H(2024)A Novel Approach to Detection of Alzheimer’s Disease from Handwriting: Triple Ensemble Learning ModelGazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji10.29109/gujsc.138641612:1(214-223)Online publication date: 25-Mar-2024
https://doi.org/10.29109/gujsc.1386416
Cerasi CBalcioğlu YHuseynov FKilic A(2024)ALGORITMO DE APRENDIZAGEM PROFUNDA COMPREENSIVA PARA COMPREENDER O PAPEL DAS REDES SOCIAIS NA PERCEPÇÃO DO CONSUMIDOR EM RELAÇÃO AO CONSUMO SUSTENTÁVELRevista de Administração de Empresas10.1590/s0034-759020240408x64:4Online publication date: 2024
https://doi.org/10.1590/s0034-759020240408x
Cerasi CBalcioğlu YHuseynov FKilic A(2024)A COMPREHENSIVE DEEP LEARNING ALGORITHM TO UNDERSTAND THE ROLE OF SOCIAL MEDIA IN CONSUMER PERCEPTION OF GREEN CONSUMPTIONRevista de Administração de Empresas10.1590/s0034-75902024040864:4Online publication date: 2024
https://doi.org/10.1590/s0034-759020240408
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