A Novel Two-Stage Feature Selection Framework for Efficiently Diagnosing Alzheimer's Disease

Research focus on the trade-off between diagnostic accuracy for Alzheimer's disease (AD) and time management for diagnosis is very limited. This study proposes a novel two-stage feature selection framework integrating variance inflation factor (VIF) and NSGA-II optimization algorithm to obtain a set of sub-items of cognitive and neuropsychological assessments (CNAs) with high diagnostic accuracy and short managing time. What sets the proposed framework apart is that it 1) mitigates multicollinearity issues caused by multiple types of feature selection algorithms employed on multiple CNAs and 2) has the multi-objective optimization capacity of searching Pareto solutions in a reduced decision space. Crucially, we design a VIF-based fast forward search for feature selection which takes into account the cost of managing time for CNAs. Experimental results demonstrate a significantly shortened managing time of 905 seconds (reduced by two-third), with a slightly improved AUC performance of 0.9113 for classifying cognitively normal controls, mild cognitive impairments, and AD patients based on a set of selected combination of sub-items compared to their involved full CNAs, thereby offering promising prospects for accurate diagnosis and fast assessment in clinical practice.