Personalized screening and risk profiles for Mild Cognitive Impairment via a Machine Learning Framework: Implications for general practice.

OBJECTIVES: Diagnosis of Mild Cognitive Impairment (MCI) requires lengthy diagnostic procedures, typically available at tertiary Health Care Centers (HCC). This prospective study evaluated a flexible Machine Learning (ML) framework toward identifying persons with MCI or dementia based on information that can be readily available in a primary HC setting.
METHODS: Demographic and clinical data, informant ratings of recent behavioral changes, self-reported anxiety and depression symptoms, subjective cognitive complaints, and Mini Mental State Examination (MMSE) scores were pooled from two aging cohorts from the island of Crete, Greece (N = 763 aged 60-93 years) comprising persons diagnosed with MCI (n = 277) or dementia (n = 153), and cognitively non-impaired persons (CNI, n = 333). A Balanced Random Forest Classifier was used for classification and variable importance-based feature selection in nested cross-validation schemes (CNI vs MCI, CNI vs Dementia, MCI vs Dementia). Global-level model-agnostic analyses identified predictors displaying nonlinear behavior. Local level agnostic analyses pinpointed key predictor variables for a given classification result after statistically controlling for all other predictors in the model.
RESULTS: Classification of MCI vs CNI was achieved with improved sensitivity (74 %) and comparable specificity (73 %) compared to MMSE alone (37.2 % and 94.3 %, respectively). Additional high-ranking features included age, education, behavioral changes, multicomorbidity and polypharmacy. Higher classification accuracy was achieved for MCI vs Dementia (sensitivity/specificity = 87 %) and CNI vs Dementia (sensitivity/specificity = 94 %) using the same set of variables. Model agnostic analyses revealed notable individual variability in the contribution of specific variables toward a given classification result.
CONCLUSIONS: Improved capacity to identify elderly with MCI can be achieved by combining demographic and medical information readily available at the PHC setting with MMSE scores, and informant ratings of behavioral changes. Explainability at the patient level may help clinicians identify specific predictor variables and patient scores to a given prediction outcome toward personalized risk assessment.