IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i10p1767-d821165.html
   My bibliography  Save this article

Prediction of Medical Conditions Using Machine Learning Approaches: Alzheimer’s Case Study

Author

Listed:
  • Georgiana Ingrid Stoleru

    (Faculty of Computer Science, Alexandru Ioan Cuza University, 700483 Iasi, Romania)

  • Adrian Iftene

    (Faculty of Computer Science, Alexandru Ioan Cuza University, 700483 Iasi, Romania)

Abstract

Alzheimer’s Disease (AD) is a highly prevalent condition and most of the people suffering from it receive the diagnosis late in the process. The diagnosis is currently established following an evaluation of the protein biomarkers in cerebrospinal fluid (CSF), brain imaging, cognitive tests, and the medical history of the individuals. While diagnostic tools based on CSF collections are invasive, the tools used for acquiring brain scans are expensive. Taking these into account, an early predictive system, based on Artificial Intelligence (AI) approaches, targeting the diagnosis of this condition, as well as the identification of lead biomarkers becomes an important research direction. In this survey, we review the state-of-the-art research on machine learning (ML) techniques used for the detection of AD and Mild Cognitive Impairment (MCI). We attempt to identify the most accurate and efficient diagnostic approaches, which employ ML techniques and therefore, the ones most suitable to be used in practice. Research is still ongoing to determine the best biomarkers for the task of AD classification. At the beginning of this survey, after an introductory part, we enumerate several available resources, which can be used to build ML models targeting the diagnosis and classification of AD, as well as their main characteristics. After that, we discuss the candidate markers which were used to build AI models with the best results in terms of diagnostic accuracy, as well as their limitations.

Suggested Citation

  • Georgiana Ingrid Stoleru & Adrian Iftene, 2022. "Prediction of Medical Conditions Using Machine Learning Approaches: Alzheimer’s Case Study," Mathematics, MDPI, vol. 10(10), pages 1-20, May.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1767-:d:821165
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/10/1767/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/10/1767/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rebecca Craig-Schapiro & Max Kuhn & Chengjie Xiong & Eve H Pickering & Jingxia Liu & Thomas P Misko & Richard J Perrin & Kelly R Bales & Holly Soares & Anne M Fagan & David M Holtzman, 2011. "Multiplexed Immunoassay Panel Identifies Novel CSF Biomarkers for Alzheimer's Disease Diagnosis and Prognosis," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-15, April.
    2. Paul T E Cusack, 2020. "The Human Brain," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 31(3), pages 24261-24266, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dominic Holland & Oleksandr Frei & Rahul Desikan & Chun-Chieh Fan & Alexey A Shadrin & Olav B Smeland & V S Sundar & Paul Thompson & Ole A Andreassen & Anders M Dale, 2020. "Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model," PLOS Genetics, Public Library of Science, vol. 16(5), pages 1-30, May.
    2. Julia Berezutskaya & Zachary V Freudenburg & Umut Güçlü & Marcel A J van Gerven & Nick F Ramsey, 2020. "Brain-optimized extraction of complex sound features that drive continuous auditory perception," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-34, July.
    3. Abigail B. Schneider & Bridget Leonard, 2022. "From anxiety to control: Mask‐wearing, perceived marketplace influence, and emotional well‐being during the COVID‐19 pandemic," Journal of Consumer Affairs, Wiley Blackwell, vol. 56(1), pages 97-119, March.
    4. Geonhui Lee & Woong Choi & Hanjin Jo & Wookhyun Park & Jaehyo Kim, 2020. "Analysis of motor control strategy for frontal and sagittal planes of circular tracking movements using visual feedback noise from velocity change and depth information," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.
    5. Odelaisy León-Triana & Julián Pérez-Beteta & David Albillo & Ana Ortiz de Mendivil & Luis Pérez-Romasanta & Elisabet González-Del Portillo & Manuel Llorente & Natalia Carballo & Estanislao Arana & Víc, 2021. "Brain Metastasis Response to Stereotactic Radio Surgery: A Mathematical Approach," Mathematics, MDPI, vol. 9(7), pages 1-19, March.
    6. Mirren Charnley & Saba Islam & Guneet K. Bindra & Jeremy Engwirda & Julian Ratcliffe & Jiangtao Zhou & Raffaele Mezzenga & Mark D. Hulett & Kyunghoon Han & Joshua T. Berryman & Nicholas P. Reynolds, 2022. "Neurotoxic amyloidogenic peptides in the proteome of SARS-COV2: potential implications for neurological symptoms in COVID-19," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    7. Samy Castro & Wael El-Deredy & Demian Battaglia & Patricio Orio, 2020. "Cortical ignition dynamics is tightly linked to the core organisation of the human connectome," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-23, July.
    8. Nguyen, Ha Trong & Brinkman, Sally & Le, Huong Thu & Zubrick, Stephen R. & Mitrou, Francis, 2022. "Gender differences in time allocation contribute to differences in developmental outcomes in children and adolescents," Economics of Education Review, Elsevier, vol. 89(C).
    9. Gregor Wolbring, 2022. "Auditing the ‘Social’ of Quantum Technologies: A Scoping Review," Societies, MDPI, vol. 12(2), pages 1-38, March.
    10. April R. Kriebel & Joshua D. Welch, 2022. "UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    11. Boada, Júlia Pareto & Maestre, Begoña Román & Genís, Carme Torras, 2021. "The ethical issues of social assistive robotics: A critical literature review," Technology in Society, Elsevier, vol. 67(C).
    12. Hamed Nili & Alexander Walther & Arjen Alink & Nikolaus Kriegeskorte, 2020. "Inferring exemplar discriminability in brain representations," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    13. Valtteri Arstila & Alexandra L Georgescu & Henri Pesonen & Daniel Lunn & Valdas Noreika & Christine M Falter-Wagner, 2020. "Event timing in human vision: Modulating factors and independent functions," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-22, August.
    14. Don, Arjuna P.H. & Peters, James F. & Ramanna, Sheela & Tozzi, Arturo, 2021. "Quaternionic views of rs-fMRI hierarchical brain activation regions. Discovery of multilevel brain activation region intensities in rs-fMRI video frames," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    15. Linzmajer, Marc & Hubert, Mirja & Hubert, Marco, 2021. "It’s about the process, not the result: An fMRI approach to explore the encoding of explicit and implicit price information," Journal of Economic Psychology, Elsevier, vol. 86(C).
    16. Natalie J Shook & Barış Sevi & Jerin Lee & Benjamin Oosterhoff & Holly N Fitzgerald, 2020. "Disease avoidance in the time of COVID-19: The behavioral immune system is associated with concern and preventative health behaviors," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
    17. Cristina Lázaro-Pérez & José Ángel Martínez-López & José Gómez-Galán, 2020. "Addictions in Spanish College Students in Confinement Times: Preventive and Social Perspective," Social Sciences, MDPI, vol. 9(11), pages 1-21, October.
    18. Yashika Arora & Pushpinder Walia & Mitsuhiro Hayashibe & Makii Muthalib & Shubhajit Roy Chowdhury & Stephane Perrey & Anirban Dutta, 2021. "Grey-box modeling and hypothesis testing of functional near-infrared spectroscopy-based cerebrovascular reactivity to anodal high-definition tDCS in healthy humans," PLOS Computational Biology, Public Library of Science, vol. 17(10), pages 1-38, October.
    19. Elvisa Drishti & Bresena Kopliku & Drini Imami, 2022. "Active political engagement, political patronage and local labour markets – The example of Shkoder," International Journal of Manpower, Emerald Group Publishing Limited, vol. 44(6), pages 1118-1142, April.
    20. Gricelda Herrera-Franco & Néstor Montalván-Burbano & Carlos Mora-Frank & Lady Bravo-Montero, 2021. "Scientific Research in Ecuador: A Bibliometric Analysis," Publications, MDPI, vol. 9(4), pages 1-34, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1767-:d:821165. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.