Quantitative Prediction of the Risk of Knee Osteoarthritis Based on Bayesian Networks
Objective:The early symptoms of knee osteoarthritis(KOA)are not obvious,therefore there are a large number of patients with knee joint lesions that have not been detected.In order to screen high-risk groups or early-stage patients with KOA on a large scale,this study established a quantitative prediction model for the risk of KOA through simple and easy-to-measure indicators.Methods:According to age and sex proportion,1045 residents from two streets in Nanjing were sampled for KOA diagnosis and physical fitness tests.The test indicators included gender,age,height,weight,BMI,thigh circumference,30-second sitting and standing,knee joint flexion,single-leg standing with eyes closed,and time-up-and-go test.In GeNle 2.3 software,a mathematical model between KOA and the above indicators was established through Bayesian network learning.The modeling steps include data discretization,structural learning using mountain climbing algorithm and K2 algorithm,parameter learning using Expectation-Maximization algorithm,model verification and sensitivity analysis.Results:Univari-ate analysis showed that there were statistically significant differences between the groups of"no KOA"and"KOA"in the 10 indicators(P＜0.01).The established mathematical model included 11 nodes and 19 directed line segments.Determining the state of any one or more nodes can predict the probability of KOA disease.In the model,gender,BMI,body weight,30-second sitting and standing,and knee joint flexion were nodes that were di-rectly related to KOA or had higher sensitivity.These indicators had high predictive value.The accuracy of the model was 78.9％,and the area under the ROC curve was 0.722.Conclusion:This study has constructed a Bayes-ian network model for quantitatively predicting the risk of developing KOA,which exhibits good predictive perfor-mance and has advantages for widespread application.
knee osteoarthritisBayesian network modelprevalence probabilitydisease risk predictionphysi-cal fitness test
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