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

Multimedia tools and applications, 2022-07, Vol.81 (18), p.25971-25992 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 ;The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. ;ISSN: 1380-7501 ;EISSN: 1573-7721 ;DOI: 10.1007/s11042-022-12754-x

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  • Title:
    Dementia classification using MR imaging and clinical data with voting based machine learning models
  • Author: Bharati, Subrato ; Podder, Prajoy ; Thanh, Dang Ngoc Hoang ; Prasath, V. B. Surya
  • Subjects: Accuracy ; Classification ; Classifiers ; Computer Communication Networks ; Computer Science ; Data Structures and Information Theory ; Decision trees ; Dementia ; Feature selection ; Machine learning ; Magnetic resonance imaging ; Multimedia Information Systems ; Recall ; Special Purpose and Application-Based Systems ; Voting
  • Is Part Of: Multimedia tools and applications, 2022-07, Vol.81 (18), p.25971-25992
  • Description: 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.
  • Publisher: New York: Springer US
  • Language: English
  • Identifier: ISSN: 1380-7501
    EISSN: 1573-7721
    DOI: 10.1007/s11042-022-12754-x
  • Source: AUTh Library subscriptions: ProQuest Central
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