Machine learning approaches for early detection of Cognitive decline: A comparative study using the OASIS-3 dataset
Cognitive decline associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI) represents one of the most critical public health challenges of the twenty-first century, affecting tens of millions of people worldwide. This paper presents a comparative study of machine learning algorithms applied to early detection of cognitive decline using the open access series of imaging studies (OASIS-3) dataset, a longitudinal multimodal neuroimaging and clinical cohort of 1378 participants aged 42-95 years. To avoid data leakage, all features directly encoding a clinical diagnosis, including clinical dementia rating (CDR) scores, diagnostic (DX) codes, and mini-mental state examination (MMSE) results, are excluded; instead, fifteen structural neuroimaging features describing brain morphology (e.g., hippocampal and entorhinal measurements) and neuropsychological sub-test features are selected via a two-stage recursive feature elimination and greedy forward-search procedure. We evaluate six supervised models: logistic regression, support vector machine (SVM), random forest, gradient boosting, K-nearest neighbours (KNN), and CatBoost. Gradient boosting achieves the highest weighted F1-score (0.856) on the held-out test set, followed by CatBoost (0.8482); CatBoost offers approximately $2\times$ lower inference latency. A neurosymbolic post-processing stage using the Scallop probabilistic logic engine identifies 166 participants with measurable structural brain atrophy (reduction in hippocampal volume) prior to a formal clinical diagnosis, demonstrating the added value of logic-augmented classifiers for early risk stratification. Feature-importance analysis identifies white-matter hyperintensity volume (MRI-visible lesions associated with vascular and neurodegenerative damage) and entorhinal/hippocampal measurements as the dominant predictors, consistent with established neurobiological evidence.