Comparative Evaluation of Twenty Machine Learning Classifiers for Binary Dementia Detection Using Longitudinal Clinical and MRI-Derived Volumetric Features
DOI:
https://doi.org/10.29070/58zm7365Keywords:
Alzheimer’s disease, dementia screening, machine learning, ensemble classifiers, Extra Trees, OASIS, Mini-Mental State Examination, Clinical Dementia Rating, brain atrophy, class imbalance.Abstract
Alzheimer’s disease (AD) accounts for the majority of the more than 58 million dementia cases recorded worldwide, and no disease-modifying therapy is currently available. Because intervention is most effective before irreversible neuronal loss occurs, low-cost screening instruments that operate on routinely collected clinical variables are of considerable practical value. This study reports a systematic comparison of twenty supervised machine learning classifiers for binary discrimination between demented and non-demented participants using the longitudinal Open Access Series of Imaging Studies (OASIS) cohort. Nine attributes were used: age, gender, years of education, socioeconomic status, Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), estimated total intracranial volume (eTIV), normalised whole-brain volume (nWBV) and atlas scaling factor (ASF). Participants originally labelled “Converted” were merged into the demented class to yield an unambiguous two-class target. Missing continuous values were median-imputed, categorical values mode-imputed, all continuous attributes standardised to zero mean and unit variance, and principal component analysis retained the components accounting for 95% of the variance. Models were assessed under an 80:20 split with five-fold cross-validation using precision, recall, F1-score and accuracy. Tree-based ensembles dominated the ranking: the Extra Trees classifier achieved the best overall balance at 87% accuracy with 91% precision and 85% recall on the demented class, followed by hard voting (85%) and XGBoost (84%). Scaling-sensitive and independence-assuming learners — stochastic gradient descent, Bernoulli and multinomial naïve Bayes, and bagging — performed poorest, between 56% and 63%. Correlation analysis identified MMSE, CDR and nWBV as the dominant discriminative attributes. The results establish a reproducible tabular baseline against which multimodal imaging-plus-clinical architectures can be judged, and indicate that structured cognitive and volumetric measures alone support useful, but not clinically sufficient, dementia screening.
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