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Dementia classification using MR imaging and clinical data with voting based machine learning models

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Abstract

Dementia is one of the leading causes of severe cognitive decline, it induces memory loss and impairs the daily life of millions of people worldwide. In this work, we consider the classification of dementia using magnetic resonance (MR) imaging and clinical data with machine learning models. We adapt univariate feature selection in the MR data pre-processing step as a filter-based feature selection. Bagged decision trees are also implemented to estimate the important features for achieving good classification accuracy. Several ensemble learning-based machine learning approaches, namely gradient boosting (GB), extreme gradient boost (XGB), voting-based, and random forest (RF) classifiers, are considered for the diagnosis of dementia. Moreover, we propose voting-based classifiers that train on an ensemble of numerous basic machine learning models, such as the extra trees classifier, RF, GB, and XGB. The implementation of a voting-based approach is one of the important contributions, and the performance of different classifiers are evaluated in terms of precision, accuracy, recall, and F1 score. Moreover, the receiver operating characteristic curve (ROC) and area under the ROC curve (AUC) are used as metrics for comparing these classifiers. Experimental results show that the voting-based classifiers often perform better compared to the RF, GB, and XGB in terms of precision, recall, and accuracy, thereby indicating the promise of differentiating dementia from imaging and clinical data.

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Acknowledgments

DNHT: This research was funded by University of Economics Ho Chi Minh City (UEH), Vietnam.

VBSP is supported by NCATS/NIH grant U2CTR002818, NHLBI/NIH grantU24HL148865, NIAID/NIH grant U01AI150748, Cincinnati Children’s Hospital Medical Center-Advanced Research Council (ARC)Grants 2018-2020, and the Cincinnati Children’s Research Foundation-Center for Pediatric Genomics (CPG) grants 2019-2021.

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Authors and Affiliations

  1. Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

    Subrato Bharati & Prajoy Podder

  2. Department of Information Technology, College of Technology and Design, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Dang Ngoc Hoang Thanh

  3. Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, 45221, USA

    V. B. Surya Prasath

  4. Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 45229, USA

    V. B. Surya Prasath

  5. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45257, USA

    V. B. Surya Prasath

  6. Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, 45267, USA

    V. B. Surya Prasath

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  1. Subrato Bharati
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Correspondence to Dang Ngoc Hoang Thanh.

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Bharati, S., Podder, P., Thanh, D.N.H. et al. Dementia classification using MR imaging and clinical data with voting based machine learning models. Multimed Tools Appl 81, 25971–25992 (2022). https://doi.org/10.1007/s11042-022-12754-x

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