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Predictive Modeling of the Risk of Hyperglycemia in Psoriasis Patients Using Machine Learning: A Multicenter
Mengyan Hu1, Dingyuan Chen2, Jian Yu2
1Department of Dermatology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People's Republic of China.
Clinical, Cosmetic and Investigational Dermatology
|January 5, 2026
Summary
Machine learning models can predict hyperglycemia risk in psoriasis patients. An Extreme Gradient Boosting model shows strong performance, aiding personalized treatment for better health outcomes.
Area of Science:
- Dermatology and Computational Medicine
- Application of Machine Learning in Clinical Practice
Background:
- Psoriasis is associated with an increased risk of hyperglycemia.
- Early identification of hyperglycemia risk in psoriasis patients is crucial for timely intervention.
- Predictive models can support clinical decision-making for managing comorbidities in psoriasis.
Purpose of the Study:
- To develop and validate machine learning (ML) models for predicting hyperglycemia risk in psoriasis patients.
- To compare the performance of eleven different ML algorithms for this prediction task.
- To identify the most effective ML model for clinical application.
Main Methods:
- Clinical data from 575 psoriasis patients were used for training and internal testing.
- An external test set of 135 psoriasis patients from NHANES was utilized for validation.
- Eleven ML algorithms were evaluated using AUC, calibration curves, and DCA.
Main Results:
- The Extreme Gradient Boosting (XGBoost) model demonstrated robust performance.
- AUC values for XGBoost were 0.821 (training), 0.820 (internal test), and 0.788 (external test).
- Calibration and DCA confirmed the model's accuracy and clinical utility; a web calculator was developed.
Conclusions:
- The XGBoost model effectively predicts hyperglycemia risk in psoriasis patients.
- This predictive capability supports personalized treatment plans for high-risk individuals.
- The model aids in managing hyperglycemia progression and psoriasis-related inflammation.
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