Automated machine learning-based prediction of the progression of knee pain, functional decline, and incidence of knee osteoarthritis in individuals at high risk of knee osteoarthritis: Data from the osteoarthritis initiative study

Abstract

Objective This study aimed to examine the performance of machine learning models in predicting the progression of knee pain, functional decline, and incidence of knee osteoarthritis (OA) in high-risk individuals, with automated machine learning (AutoML) being used to automate the prediction process. Design There were four stages in the process of our AutoML-integrated prediction. Stage 1—Data preparation: The data of 3200 eligible individuals in the Osteoarthritis Initiative (OAI) study who were considered at high risk of knee OA at the baseline visit were extracted and used. Specifically, 1094 variables from the OAI study were used to predict the changes in knee pain, physical function, and incidence of knee OA (i.e. the first occurrence of frequent knee symptoms and definite tibial osteophytes (Kellgren and Lawrence grade ≥2)) over a 9-year period. Stage 2—Model training: The AutoML approach was used to automatically train nine widely used machine learning (ML) models. Stage 3—Model testing: The AutoML approach was used to automatically test the performance of the ML models. Stage 4—Selection of important input variables: The AutoML approach automated the process of computing the importance scores of all input variables and identifying the most important ones, using the technique of permutation feature importance. Results Using the AutoML approach, the weighted ensemble model and the CatBoost model showed the best performance among all nine ML models. For the prediction of each outcome in each year, the five most important input variables were identified, most of which were obtained from self-reported questionnaire surveys and radiographic imaging reports. Conclusion The AutoML approach has shown potential in automating the process of using ML models to predict long-term changes in knee OA-related outcomes. Its use could support the deployment of ML solutions, facilitating the provision of personalized interventions to prevent the deterioration of knee health and incident knee OA.