Dementia ascertainment in India and development of nation-specific cutoffs: A machine learning and diagnostic analysis

Abstract

Introduction: Cognitive assessments are useful in ascertaining dementia but may be influenced by patient characteristics. India's distinct culture and demographics warrant investigation into population-specific cutoffs. Methods: Data were utilized from the Longitudinal Aging Study in India-Diagnostic Assessment of Dementia (n = 2528). Dementia ascertainment was conducted by an online panel. A machine learning (ML) model was trained on these classifications, with explainable artificial intelligence to assess feature importance and inform cutoffs that were assessed across demographic groups. Results: The Informant Questionnaire of Cognitive Decline in the Elderly (IQCODE) and Hindi Mini-Mental State Examination (HMSE) were identified as the most impactful assessments with optimal cutoffs of 3.8 and 25, respectively. Discussion: An ML assessment of clinician dementia ratings identified IQCODE and HMSE to be the most impactful assessments. Optimal cutoffs of 3.8 and 25 were identified and performed excellently in the overall sample, though did decrease in specific, more difficult-to-diagnose subgroups. Highlights: Pioneers use of explainable artificial intelligence in the diagnosis of dementia. Creates assessment cutoffs specific to the nation of India. Highlights differences in cutoffs across nations.