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Genetic algorithm with logistic regression feature selection for Alzheimer’s disease classification

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Abstract

Cognitive impairment must be diagnosed in Alzheimer’s disease as early as possible. Early diagnosis allows the person to receive effective treatment benefits apart from helping him or her to remain independent longer. In this paper, different feature selection techniques are utilized with different classifiers in the classification of this chronic disease as normal control (NC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD) based on the MRI images of ADNI dataset. Dimensionality reduction plays a major role in improving classification performance when there are fewer records with high dimensions. After different trials to select the ample features, support vector machine (SVM) with radial basis function kernel is found to produce better results with 96.82%, 89.39% and 90.40% accuracy for binary classification of NC/AD, NC/MCI and MCI/AD, respectively, with repeated tenfold stratified cross-validation. Combining mini-mental state examination (MMSE) score to the MRI data, there has been an improvement of 2.7% in the MCI/AD classification, but it does not have much influence in the NC/AD and NC/MCI classification.

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Acknowledgements

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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  1. Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, 626 005, India

    R. Divya & R. Shantha Selva Kumari

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  1. R. Divya
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the Alzheimer’s Disease Neuroimaging Initiative

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Correspondence to R. Divya.

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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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Divya, R., Shantha Selva Kumari, R. & the Alzheimer’s Disease Neuroimaging Initiative. Genetic algorithm with logistic regression feature selection for Alzheimer’s disease classification. Neural Comput & Applic 33, 8435–8444 (2021). https://doi.org/10.1007/s00521-020-05596-x

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