MACHINE LEARNING APPROACHES FOR EARLY DETECTION OF COGNITIVE DECLINE IN NEUROPSYCHIATRIC DISORDERS

Abstract

Early detection of cognitive decline in neuropsychiatric disorders remains a critical challenge, as traditional clinical assessments often fail to capture subtle impairments that emerge during the earliest phases of disease progression. This study proposes a multimodal machine-learning framework designed to identify early cognitive deterioration by analyzing neurocognitive performance markers, behavioral response-time variability, linguistic and speech-derived features, and neurophysiological signal complexity. After rigorous preprocessing and feature extraction, a set of supervised learning models was trained to distinguish cognitively stable individuals from those exhibiting early signs of decline. The system demonstrated strong predictive performance, with latent-feature clustering and attention-based interpretability indicating that the models successfully captured nonlinear and multidimensional patterns associated with early impairment. Expert clinical review further validated the meaningful alignment between model-identified features and known cognitive biomarkers linked to executive dysfunction, memory instability, and semantic degradation. Although limitations exist related to dataset size and recording variability, the results provide compelling evidence that machine-learning approaches can significantly improve proactive detection, risk stratification, and monitoring of cognitive decline in neuropsychiatric populations. These findings highlight the potential of AI-driven cognitive analytics to support precision mental-health care and enable earlier, more targeted interventions.