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

ABSTRACT:

Background

Psoriasis is a chronic immune-mediated inflammatory skin disorder characterized by multifactorial pathogenesis. Recent studies have extensively highlighted the strong associations between psoriasis and various inflammatory markers, which are considered novel predictive tools for evaluating systemic inflammation.

Methods

Cross-sectional data from the NHANES were analyzed in this study. To assess model performance and generalizability, the dataset was randomly divided into 70% for training and 30% for validation. To address class imbalance in the training data, a hybrid resampling technique (SMOTEENN) was applied. Subsequently, nine classification algorithms were developed using the processed training set, including random forest, neural networks, XGBoost, k-nearest

SUBMITTER: Yang L 

PROVIDER: S-EPMC12350470 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature