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A novel approach to enhance feature selection using linearity assessment with ordinary least squares regression for Alzheimer’s Disease stage classification

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Abstract

Diagnosing Alzheimer’s disease (AD) in its prodromal stage is a significantly crucial area of research. Approximately 50% of individuals within the well-known Mild Cognitive Impairment (MCI) cohort are estimated to progress to AD, and the factors influencing conversion remain unknown. Gaining insights into the disease evolution can enhance support strategies and potentially slow down the pathology. Utilizing the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, our objective is to construct a framework for distinguishing between Normal Controls (NC) and different stages of Alzheimer’s Disease (AD), encompassing Earlier Mild Cognitive Impairment (EMCI), Later Mild Cognitive Impairment (LMCI), and AD patients. In pursuit of this objective, we preprocessed Diffusion Tensor and Magnetic Resonance brain images from 237 subjects, generating corresponding brain connectivity maps. Notably, we introduce an innovative linearity assessment method that utilizes the Ordinary Least Squares (OLS) linear regression model to identify and select relevant features for classification. This approach effectively identifies features with strong linear relationships to the target variable. Our method’s superiority is demonstrated through a comparative analysis with the traditional SelectKBest approach. By integrating this feature selection strategy with a Logistic Regression model, our study achieves both efficient and highly accurate classification outcomes, highlighting the effectiveness of the proposed method. In a four-class classification scenario, the model attained an accuracy of \(66\% \pm 0.06\). In binary classification, the results were equally impressive, with an area under the curve of \(0.68\pm 0.10 \%\) for CN vs. EMCI discrimination, \(99\pm 0.02\% \)for distinguishing LMCI from adjacent classes CN and EMCI, and \(0.79\% \pm 0.08\) for discriminating AD from healthy subjects. Additionally, the calculation of Pearson’s correlation coefficient has been employed to identify cortical regions affected by changes, explore the nature of fiber disconnection propagation from one stage to another, and establish the traceability of the interference origin between stages. The summarized results reveal an apparent flow of white matter disruption from the right to the left hemisphere.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through a large group Research Project under grant number RGP2/390/45.

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Author notes

  1. Nadia Bouattour, Noura Mabrouki, Lamya Sallemi and Ahmed Ben Hamida contributed equally to this work.

Authors and Affiliations

  1. Advanced Technologies for Medicine and Signals ATMS,Department of Electrical and Computer Engineering, National Engineers School, Sfax, Tunisia

    Besma Mabrouk & Lamia Sellami

  2. Department of Neurology, University Hospital Habib Bourguiba, Sfax, Tunisia

    Nadia Bouattour

  3. Department of Psychiatry, University Hospital Taher Sfar, Mahdia, Tunisia

    Noura Mabrouki

  4. Department IS, College of Computer Science, King Khaled Universiy, ‘KKU’, Abha, Kingdom of Saudi Arabia, Saudi Arabia

    Ahmed Ben Hamida

  5. http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

    for the Alzheimer’s Disease Neuroimaging Initiative

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Contributions

BM and ABH were responsible for the conception and study design. Statistical analysis was conducted by BM , and NM, while the interpretation of results involved BM, NM, NB, and LS. BM managed the dataset acquisition and preprocessing, as well as the development and implementation of the coding. BM, and NM drafted the manuscript and revised it critically for important intellectual content. All authors approved the final version to be published and agreed to be accountable for the integrity and accuracy of all aspects of the work.

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Correspondence to Besma Mabrouk.

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Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:(http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf) .

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Mabrouk, B., Bouattour, N., Mabrouki, N. et al. A novel approach to enhance feature selection using linearity assessment with ordinary least squares regression for Alzheimer’s Disease stage classification. Multimed Tools Appl 83, 86059–86078 (2024). https://doi.org/10.1007/s11042-024-20254-3

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