Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain

This research explored how to predict mild cognitive impairment (MCI) in older adults with chronic pain using machine learning models. It identified key risk factors like pain level, age, depression, and sleep quality. The most accurate model, XGBoost, achieved a 92.5% prediction accuracy and highlighted how these factors influence cognitive health. By enabling early detection of MCI, this study equips nurses with tools to personalize care and mitigate progression to dementia. The research emphasizes the importance of integrating advanced analytics into healthcare to address aging-related challenges and improve cognitive performance in vulnerable populations.

To improve brain health, older adults with chronic pain should prioritize pain management, engage in regular mental stimulation, maintain healthy sleep patterns, and seek help for depression. This advice aligns with study findings, but given its cross-sectional design, causality isn't established. Individuals should consult healthcare professionals for personalized guidance. Adopting these measures may reduce MCI risk and promote cognitive resilience.