What question did this study set out to answer?

The aim is to create a prediction model for fall fear in knee osteoarthritis patients using synchronous optimization techniques.

March 1, 2026Open Access

Personalizing fall fear prevention in knee osteoarthritis: an interpretable prediction framework via IGKSO synchronous optimization and SHAP-based risk stratification

Key Points

The aim is to create a prediction model for fall fear in knee osteoarthritis patients using synchronous optimization techniques.
Selected 541 knee osteoarthritis patients from two hospitals for evaluation.
Developed a prediction model utilizing an improved swarm intelligence algorithm.
Divided data into training (80%) and testing (20%) sets for model validation.
Used SHAP analysis to interpret and analyze selected risk factors.
Achieved a maximum F1 score of 0.8842 and an area under the curve of 0.9451 in the training set.
The test set reached an F1 score of 0.8589 and an area under the curve of 0.9315.
Identified eight key variables influencing fall fear, including TUG time and anxiety scores.
Revealed that older adult females with specific high-risk indicators constituted the peak-risk group.

Abstract

Objective To construct a concern about falling (CAF) prediction model for patients with knee osteoarthritis (KOA) based on synchronous optimization. Methods A total of 541 patients with KOA admitted to two hospital from September 2021 to September 2023 were selected. CAF was evaluated using the Falls Efficacy Scale-International (FES-I). Patients were divided into a CAF group ( n = 360, FES-I ≥ 28 points) and a no CAF group ( n = 181, FES-I 28 points). 80% of the data (433 cases) were used as the training set, and the remaining 20% (108 cases) were used as the test set. An improved swarm intelligence algorithm was used for feature selection and hyperparameter optimization. The selected variables were further analyzed by Shapley Additive exPlanation (SHAP) interpretable method. Results In the training set, the maximum F1 score of the improved synchronous optimization machine learning model was 0.8842, and the area under the curve reached 0.9451. In the test set, the maximum F1 score of the improved synchronous optimization machine learning model was 0.8589, and the area under the curve reached 0.9315. Eight variables were selected based on the improved synchronous optimization machine learning model, including Timed Up-and-Go (TUG) time, Western Ontario and McMaster Universities Osteoarthritis (WOMAC) pain score, Hospital Anxiety and Depression Scale (HADS) anxiety score, knee extensor moment, age, sex, Kellgren-Lawrence (KL) grade, and Body mass index (BMI). Critically, SHAP analysis demonstrated triadic interactive effects among key risk indicators, revealing that older adult female patients with concurrent TUG time 14 s, HADS-anxiety scores 10, and high WOMAC pain scores constituted the peak-risk cohort amplified through bio-psycho-social interactions. Conclusion This study validated a multimodal predictor model for CAF in knee osteoarthritis (KOA) patients through a machine learning framework, revealing synergistic mechanisms among biomechanical, psychological, and social dynamics, with specific risk stratification for high-risk populations such as older adult females to guide clinical practice.

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Cite This Study

Yin et al. (Thu,) studied this question.

synapsesocial.com/papers/69a3d747ec16d51705d2dc4f https://doi.org/https://doi.org/10.3389/fpubh.2026.1749921

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