โก Quick Summary
This study developed a concern about falling (CAF) prediction model for patients with knee osteoarthritis (KOA) using an innovative machine learning approach. The model achieved a maximum F1 score of 0.8842 and an AUC of 0.9451, highlighting its potential for personalized fall fear prevention strategies.
๐ Key Details
- ๐ Dataset: 541 patients with KOA from two hospitals
- ๐งฉ Features used: Timed Up-and-Go (TUG) time, WOMAC pain score, HADS anxiety score, and more
- โ๏ธ Technology: Improved synchronous optimization machine learning model
- ๐ Performance: Training set F1 score: 0.8842, AUC: 0.9451; Test set F1 score: 0.8589, AUC: 0.9315
๐ Key Takeaways
- ๐ The study focused on predicting fall fear in KOA patients.
- ๐ก Machine learning was utilized for feature selection and hyperparameter optimization.
- ๐ฉโ๐ฌ Eight key variables were identified as significant risk factors for fall fear.
- ๐ The model demonstrated high performance in both training and test datasets.
- ๐ค SHAP analysis revealed complex interactions among risk indicators.
- ๐ Older adult females with specific risk profiles were identified as peak-risk individuals.
- ๐ The study was conducted from September 2021 to September 2023.
๐ Background
Knee osteoarthritis (KOA) is a prevalent condition that significantly impacts mobility and quality of life. One of the critical concerns for KOA patients is the fear of falling, which can lead to reduced physical activity and further deterioration of health. Understanding and predicting this fear is essential for developing effective prevention strategies.
๐๏ธ Study
Conducted over two years, this study aimed to construct a robust prediction model for concern about falling (CAF) in KOA patients. A total of 541 patients were evaluated using the Falls Efficacy Scale-International (FES-I), and the data was split into training and test sets to validate the model’s performance.
๐ Results
The improved synchronous optimization machine learning model achieved impressive results, with a maximum F1 score of 0.8842 and an AUC of 0.9451 in the training set. In the test set, the model maintained a strong performance with an F1 score of 0.8589 and an AUC of 0.9315. These metrics indicate a high level of accuracy in predicting fall fear among KOA patients.
๐ Impact and Implications
The findings from this study have significant implications for clinical practice. By identifying high-risk populations, particularly older adult females with specific risk factors, healthcare providers can tailor interventions to mitigate fall fear. This personalized approach could enhance patient outcomes and improve overall quality of life for those living with KOA.
๐ฎ Conclusion
This research highlights the potential of machine learning in predicting fall fear among KOA patients. The development of a multimodal predictor model not only advances our understanding of the interplay between biomechanical, psychological, and social factors but also paves the way for targeted interventions. Continued exploration in this area is essential for improving care and outcomes for patients with knee osteoarthritis.
๐ฌ Your comments
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Personalizing fall fear prevention in knee osteoarthritis: an interpretable prediction framework via IGKSO synchronous optimization and SHAP-based risk stratification.
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 โฅ 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.
Author: [‘Yin M’, ‘Fang W’, ‘Cheng Y’, ‘Feng Y’]
Journal: Front Public Health
Citation: Yin M, et al. Personalizing fall fear prevention in knee osteoarthritis: an interpretable prediction framework via IGKSO synchronous optimization and SHAP-based risk stratification. Personalizing fall fear prevention in knee osteoarthritis: an interpretable prediction framework via IGKSO synchronous optimization and SHAP-based risk stratification. 2026; 14:1749921. doi: 10.3389/fpubh.2026.1749921