Abstract
Background
Although various factors were reported to be related to post‐herpetic neuralgia (PHN), studies based on adequate and comprehensive data were absent.Methods
Data was extracted from cases of hospitalized patients with herpes zoster in dermatology department, Sichuan hospital of traditional Chinese medicine range from December, 2011 to February, 2018, and then cleaned to build prediction model with TREENET algorithms. Following evaluated the prediction model by ROC and confusion matrix, variables importance ranking and variables dependency analysis were performed, resulting in the importance ranking of factors for PHN and the dependency between factors and PHN.Results
Based on strict inclusion and exclusion criteria, 1303 (571 PHN and 732 normal controls) cases and 2958 indicators were selected. Model evaluation showed high ROC value (training sample = 0.985, test samples = 0.752) and high accuracy value (70.27%), which indicated that the model was predictive. After variables importance ranking and variables dependency analysis, 62 variables in the model were associated with the occurrence of PHN.Conclusions
Our study identified 62 variables related to PHN and revealed that various variables were the important risk factors for PHN, including age, MCHC, sodium and UA.This article is protected by copyright. All rights reserved.
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