Volume 22 Issue 10
Oct.  2024
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Article Navigation >  Chinese Journal of General Practice  > 2024  >  22(10): 1656-1659
XU Ruiyao, YAN Xiaoning, HUANG Yongmei, JING Huiling, ZHAO Chan, WANG Yasha, SUN Liping, ZHAO Yiding. Establishment of a prediction model of risk factors for psoriasis vulgaris based on machine learning[J]. Chinese Journal of General Practice, 2024, 22(10): 1656-1659. doi: 10.16766/j.cnki.issn.1674-4152.003705
Citation: XU Ruiyao, YAN Xiaoning, HUANG Yongmei, JING Huiling, ZHAO Chan, WANG Yasha, SUN Liping, ZHAO Yiding. Establishment of a prediction model of risk factors for psoriasis vulgaris based on machine learning[J]. Chinese Journal of General Practice, 2024, 22(10): 1656-1659. doi: 10.16766/j.cnki.issn.1674-4152.003705
shu

Establishment of a prediction model of risk factors for psoriasis vulgaris based on machine learning

doi: 10.16766/j.cnki.issn.1674-4152.003705
  • 1.

    Department of Dermatology, Shaanxi Traditional Chinese Medicine Hospital, Xi'an, Shaanxi 710003, China

Funds:

 SZY-KJCYC-2023-005

 2021ZDLSF04-12

 2019ZDLSF04-08

  • Received Date: 2024-02-28
    Available Online: 2024-12-28
    Abstract
  •   Objective  This study aims to identify sensitive indicators and their combinations to distinguish psoriasis, providing support for augmenting diagnostic evaluations and clinical decisions.  Methods  A total of 301 patients with psoriasis vulgaris from multiple centers, who visited from November 1, 2019 to October 31, 2021, were included. Data were collected on general characteristics, psoriasis scale information, TCM syndrome, and laboratory tests. Six machine learning algorithms were used to construct and verify the classification model for predicting mild and severe psoriasis. The classification performance of the models was evaluated by the parameters of receiver operating characteristic (ROC) area under the curve (AUC), precision, recall, and accuracy.  Result  The Naive Bayesian Model achieve an AUC of 0.801 4, while the AUC values of the other implicit classification models of logistic regression, SVM, random forest, AdaBoost, and SGD were 0.759 1, 0.754 6, 0.800 4, 0.712 6, and 0.773 7, respectively. Both Bayesian and random forest models had true positive rates of more than 80% in the ROC curve.  Conclusion  "DLQI", "TCM syndrome", "Baso", "PDW", and "Onset season" were found as key indicators for predicting psoriasis severity. Machine learning and data mining can use real-world clinical data to develop predictive models for psoriasis risk factors, providing a basis for early disease assessment and medical decision-making.

     

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