Construction of risk prediction model for depressive state in patients with postherpetic neuralgia based on machine learning algorithm
Objective To construct a risk prediction model for depression in the patients with posther-petic neuralgia(PHN)based on machine learning algorithm to provide a new idea and method for accurate prediction of depressive state occurrence in clinical PHN patients.Methods The inpatients with PHN in the Second Affiliated Hospital of Army Military Medical University from June 2022 to June 2023 were selected as the study subjects and randomly divided into the training set and test set according to the ratio of 8∶2,and whether or not the depressive state occurring served as the outcome variable.Based on six machine learning al-gorithms of K-Nearest Neighbor(KNN),Decision Tree(DT),Logistic Regression(LR),Naive Bayes(NB),Random Forest(RF)and Support Vector Machine(SVM),a risk prediction model for PHN patients with complicating depressive state was constructed.The model performance was evaluated based on the area under the curve(AUC),accuracy,precision,recall rate and F1 score,and the optimal model was selected.Results A total of 275 PHN patients were included,among them 170 cases developed the depressive state,and the inci-dence rate of depressive state was 61.82％.The AUC values of KNN,DT,LR,NB,RF and SVM models were 0.574,0.589,0.760,0.742,0.591 and 0.733,respectively,among which the AUC value,accuracy,precision,recall rate and F1 score of LR model were the highest.Conclusion The risk prediction model of PHN compli-cating depressive state based on LR machine learning algorithm has the best performance,which is helpful for early clinical assessment and prevention of depressive state.
postherpetic neuralgiamachine learningdepressive staterisk factorprediction model
万方数据
维普
