Ensemble Merit Merge Feature Selection for Enhanced Multinomial Classification in Alzheimer’s Dementia

Abstract

The objective of this study is to develop an ensemble classifier with Merit Merge feature selection that will enhance efficiency of classification in a multivariate multiclass medical data for effective disease diagnostics. The large volumes of features extracted from brain Magnetic Resonance Images and neuropsychological tests for diagnosis lead to more complexity in classification procedures. A higher level of objectivity than what readers have is needed to produce reliable dementia diagnostic techniques. Ensemble approach which is trained with features selected from multiple biomarkers facilitated accurate classification when compared with conventional classification techniques. Ensemble approach for feature selection is experimented with classifiers like Naïve Bayes, Random forest, Support Vector Machine, and C4.5. Feature search is done with Particle Swarm Optimisation to retrieve the subset of features for further selection with the ensemble classifier. Features selected by the proposed C4.5 ensemble classifier with Particle Swarm Optimisation search, coupled with Merit Merge technique (CPEMM), outperformed bagging feature selection of SVM, NB, and Random forest classifiers. The proposed CPEMM feature selection found the best subset of features that efficiently discriminated normal individuals and patients affected with Mild Cognitive Impairment and Alzheimer's Dementia with 98.7% accuracy.

Evaluation of Brain Tortuosity Measurement for the Automatic Multimodal Classification of Subjects with Alzheimer’s Disease

“…Sivapriya et al [12] reported a research with three studied populations (AD, MCI, and HC). is study proposes an ensemble feature selection approach using different classifiers with a particle swarm optimization search strategy and the merit merge technique.…”

Random Forest Feature Selection, Fusion and Ensemble Strategy: Combining Multiple Morphological MRI Measures to Discriminate among healthy elderly, MCI, cMCI and Alzheimer's disease patients: from the Alzheimer’s disease neuroimaging initiative (ADNI) database

“…Regarding the modalities, many studies focusing on predictive biomarkers for either AD or MCI or both conditions investigated structural MRI data alone (Lebedev et al, 2014;Moradi et al, 2015;Nanni et al, 2016 ;Salvatore et al, 2015Salvatore et al, ,2016Ardekani et al, 2017;Lebedeva et al, 2017) or combined with features extracted from other modalities, like FDG-PET (Gray et al, 2013;Sivapriya et al, 2015), florbetapir-PET (Wang et al, 2016), FLAIR (Oppedal et al, 2015) and fMRI (Tripoliti et al, 2007;Son et al, 2017).…”

“…Focusing on the cohort diagnosis and the targeted groups, two studies (Tripoliti et al, 2007;Lebedev et al, 2014) investigated Alzheimer's patients (AD) and healthy controls (HC), four studies (Cabral et al, 2013;Sivapriya et al, 2015;Maggipinto et al, 2017;Son et al, 2017) examined AD, HC and Mild Cognitive Impairment (MCI), two studies (Gray et al, 2013;Moradi et al, 2015) considered AD, HC, stable MCI (sMCI) and progressive MCI (pMCI, converted to AD), two had sMCI and pMCI (Wang et al, 2016;Ardekani et al, 2017), one had HC and MCI (Lebedeva et al, 2017) and one (Oppedal et al, 2015) had AD, HC and Lewybody dementia (LBD) patients.…”

“…A total of eight studies (Tripoliti et al, 2007;Cabral et al, 2013;Lebedev et al, 2014;Moradi et al, 2015;Sivapriya et al, 2015;Ardekani et al, 2017;Lebedeva et al, 2017;Maggipinto et al, 2017) applied a feature selection strategy for reducing the dimension of the variables space. In two out of eight cases, the number of trees used in the RF was not specified , Son et al, 2017.…”

“…Two studies that adopted multi-class classifiers reported 96.3% classification accuracy for HC-MCI-AD: (Sivapriya et al, 2015) and 87% for HC-LBD-AD (Oppedal et al, 2015). However, both of them used multimodal features, in contrast to only MRI-based studies.…”