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Related Experiment Video

Updated: Sep 11, 2025

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

Published on: March 1, 2024

Exploring immune-inflammation markers in psoriasis prediction using advanced machine learning algorithms.

Li Yang¹, Shixin He¹, Li Tang¹

¹Department of Medical Cosmetology, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China.

Frontiers in Immunology

|August 18, 2025

Summary

This summary is machine-generated.

This study identified key inflammatory markers, including the monocyte-to-lymphocyte ratio (MLR), as valuable predictors for psoriasis. Machine learning models, particularly gradient boosting, effectively identified these markers for assessing systemic inflammation and predicting psoriasis development.

Keywords:

machine learning algorithms monocyte-to-lymphocyte ratio national health and nutrition examination survey neutrophil-to-monocyte ratio psoriasis

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Lymphocyte Isolation from Human Skin for Phenotypic Analysis and Ex Vivo Cell Culture

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Published on: April 8, 2016

Area of Science:

Immunodermatology
Computational Biology
Biostatistics

Background:

Psoriasis is a chronic, immune-mediated skin disease with complex causes.
Inflammatory markers are increasingly recognized for their role in predicting systemic inflammation in psoriasis patients.

Purpose of the Study:

To identify significant inflammatory markers for psoriasis prediction using machine learning.
To evaluate the performance of various classification algorithms in identifying psoriasis risk.

Main Methods:

Analysis of cross-sectional data from 22,908 participants in the NHANES dataset.
Application of a hybrid resampling technique (SMOTEENN) to handle class imbalance.
Development and comparison of nine classification algorithms, including gradient boosting and logistic regression.

Main Results:

Gradient boosting achieved the highest performance (AUC: 0.629), closely followed by logistic regression (AUC: 0.627).
The monocyte-to-lymphocyte ratio (MLR) showed the best classification performance (AUC: 0.662), followed by neutrophil-to-lymphocyte ratio (NLMR) and others.
MLR demonstrated the highest relative importance, indicating its critical role in psoriasis classification.

Conclusions:

Gradient boosting models effectively identified key inflammatory markers like MLR, SII, and NLR for psoriasis prediction.
These inflammatory markers serve as reliable indicators of systemic inflammation and potential predictors of psoriasis.
The findings highlight the clinical utility of these markers in assessing psoriasis risk and systemic inflammation.