Prognostic prediction of patients with rheumatoid arthritis combined with osteoporosis based on machine learning and interpretative models
Objective To explore the application value of machine learning and interpretive models in prognostic prediction of patients with rheumatoid arthritis combined with osteoporosis.Methods A total of 194 patients with rheumatoid arthritis combined with osteoporosis admitted to Xinjiang Uyghur Autonomous Region Uyghur Hospital from June 2021 to July 2023 were selected and divided into poor prognosis group(46 cases)and good prognosis group(148 cases)according to prognosis.The prediction models of random forest,support vector machine,naive Bayes,BP neural network,and XGBoost were con-structed by the differences of two groups of clinical data,and the influencing factors of patient prognosis were analyzed by multi-factor logistic regression model.After the optimal prediction model was selected by receiver operating characteristic(ROC)curve and PR curve,SHAP interpretation model was used to interpret its characteristics,and one case of patients was randomly selected for model evaluation.Results There were statistically significant differences in age,smoking history,oc-cupation,rheumatoid factor,antstreptolysin,IgM,erythrocyte settlement rate,glutamic oxalacetic transaminase,thermal salt packet treatment,acupuncture treatment,massage treatment,osteoporosis instrument treatment,joint function stage,patient health rating scale score,and visual simulation score between the two groups(P＜0.05).The results of multivariate analysis showed that age(OR=1.066,95％CI:1.021-1.113),occupation(OR=16.711,95％CI:5.499-50.787),the use of osteoporosis instrument(OR=6.836,95％CI:2.362-19.782),joint function stage(OR=2.756,95％CI:1.388-5.474),health assessment questionnaire score(OR=6.287,95％CI:2.514-15.718)were the independent influencing factors of poor prognosis in patients with rheumatoid arthritis combined with osteoporosis(P＜0.05).ROC and PR curve results show that the random forest prediction model had the best performance and the highest credibili-ty.SHAP interpretation model showed that the level of rheumatoid factor,patient health rating scale score,occupation,etc.,were all factors affecting the poor prognosis of patients with rheumatoid arthritis combined with osteoporosis.The results of patient model evaluation showed that rheumatoid factor level,occupation,health assessment questionnaire score,age,and whether massage treatment were the main factors affecting the prognosis of this patient.Conclusion The prognosis prediction model based on machine learning can predict the prognosis of patients with rheumatoid arthritis combined with osteoporo-sis,and can provide targeted prevention and normative treatment for related factors to reduce the occurrence of adverse prognosis.
万方数据
维普
