Predicting frailty in older patients with chronic pain using explainable machine learning: A cross-sectional study.

Abstract

Frailty is common among older adults with chronic pain, and early identification is crucial in preventing adverse outcomes like falls, disability, and dementia. However, effective tools for identifying frailty in this population remain limited. This study aimed to explore frailty risk factors in older adults with chronic pain and to develop 9 machine learning models for frailty identification. The Shapley Additive Explanations (SHAP) method was used to explain the models. The Random Forest (RF) model performed best with 0.822 accuracy, 0.797 precision, and an AUC of 0.881. The variables in the RF model included: age, BMI, education level, pain duration, number of pain sites, pain level, depression, and Activity of Daily Living (ADL). Pain level, depression, and ADL were the 3 most important variables in the RF model. This model helps healthcare providers to identify frailty early, enabling timely interventions to improve patient outcomes and promote healthy aging.