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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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.
The data used for this research work is collected from the publically available OASIS database and the authors do not violate the ethical statement.
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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