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Voxel-MARS: a method for early detection of Alzheimer’s disease by classification of structural brain MRI

Author

Listed:
  • Alper Çevik

    (Middle East Technical University)

  • Gerhard-Wilhelm Weber

    (Middle East Technical University)

  • B. Murat Eyüboğlu

    (Middle East Technical University)

  • Kader Karlı Oğuz

    (Hacettepe University)

Abstract

Neuroscience is of emerging importance along with the contributions of Operational Research to the practices of diagnosing neurodegenerative diseases with computer-aided systems based on brain image analysis. Although multiple biomarkers derived from Magnetic Resonance Imaging (MRI) data have proven to be effective in diagnosing Alzheimer’s disease (AD) and mild cognitive impairment (MCI), no specific system has yet been a part of routine clinical practice. This paper aims to introduce a fully-automated voxel-based procedure, Voxel-MARS, for detection of AD and MCI in early stages of progression. Performance was evaluated on a dataset of 508 MRI volumes gathered from the Alzheimer’s Disease Neuroimaging Initiative database. Data were transformed into a high-dimensional space through a feature extraction process. A novel 3-step feature selection procedure was applied. Multivariate Adaptive Regression Splines method was used as a classifier for the first time in the field of brain MRI analysis. The results were compared to those presented in a previous study on 28 voxel-based methods in terms of their ability to separate control normal (CN) subjects from the ones diagnosed with AD and MCI. It was observed that our method outperformed all of the others in sensitivity (83.58% in AD/CN and 78.38% in MCI/CN classification) with acceptable specificity values (over 85% in both cases). Furthermore, the method worked for discriminating MCI patients which converted to AD in 18 months (MCIc) from non-converters (MCInc) with a sensitivity outcome better than 27 of 28 methods. Overall, it was shown that the proposed method is promising in early detection of AD.

Suggested Citation

  • Alper Çevik & Gerhard-Wilhelm Weber & B. Murat Eyüboğlu & Kader Karlı Oğuz, 2017. "Voxel-MARS: a method for early detection of Alzheimer’s disease by classification of structural brain MRI," Annals of Operations Research, Springer, vol. 258(1), pages 31-57, November.
    • Handle: RePEc:spr:annopr:v:258:y:2017:i:1:d:10.1007_s10479-017-2405-7
    DOI: 10.1007/s10479-017-2405-7
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    References listed on IDEAS

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    1. Dachuan Shih & Seoung Kim & Victoria Chen & Jay Rosenberger & Venkata Pilla, 2014. "Efficient computer experiment-based optimization through variable selection," Annals of Operations Research, Springer, vol. 216(1), pages 287-305, May.
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    Cited by:

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    2. Magdalena Graczyk-Kucharska & Robert Olszewski & Gerhard-Wilhelm Weber, 2023. "The use of spatial data mining methods for modeling HR challenges of generation Z in greater Poland Region," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(1), pages 205-237, March.
    3. Ayşe Özmen & Yuriy Zinchenko & Gerhard-Wilhelm Weber, 2023. "Robust multivariate adaptive regression splines under cross-polytope uncertainty: an application in a natural gas market," Annals of Operations Research, Springer, vol. 324(1), pages 1337-1367, May.
    4. Betül Kalaycı & Ayşe Özmen & Gerhard-Wilhelm Weber, 2020. "Mutual relevance of investor sentiment and finance by modeling coupled stochastic systems with MARS," Annals of Operations Research, Springer, vol. 295(1), pages 183-206, December.
    5. Adabavazeh, Nazila & Nikbakht, Mehrdad & Tirkolaee, Erfan Babaee, 2023. "Identifying and prioritizing resilient health system units to tackle the COVID-19 pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).

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