Data Sheet 1_Exploring immune-inflammation markers in psoriasis prediction using advanced machine learning algorithms.pdf

BackgroundPsoriasis 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.
MethodsCross-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 neighbors, gradient boosting, logistic regression, naïve Bayes, AdaBoost, and SVMs. The final gradient boosting was implemented via the gbm package in R, with hyperparameters selected from the default tuning grid of the caret framework. Inflammatory biomarkers with the highest classification utility were identified based on the predictions of the best-performing model.
ResultsA total of 22,908 participants were included in the final analysis. Gradient boosting (AUC: 0.629, 95% CI: 0.588–0.669) demonstrated the highest performance, followed closely by logistic regression (AUC: 0.627, 95% CI: 0.588–0.666). Among all the inflammatory markers, MLR exhibited the best classification performance, with an AUC value of 0.662 (95% CI: 0.640–0.683), followed by NLMR, with an AUC value of 0.661 (95% CI: 0.640–0.683). Other markers, including the NLR, dNLR, SII, SIRI, and PLR, had AUC values ranging from 0.658 to 0.661. The MLR had the highest relative importance score, demonstrating its critical role in the model’s predictive performance for psoriasis classification. The NLR ranked second, followed by the SII and SIRI, which had moderate contributions, whereas the PLR contributed the least.
ConclusionsAmong all the tested algorithms, the gradient boosting model achieved the best performance. Not only does it achieve the highest predictive accuracy, but it also excels in classification efficacy and feature importance analysis, highlighting key inflammatory markers such as the MLR, SII, and NLR. These markers are significant as reliable indicators for evaluating systemic inflammation and predicting the development of psoriasis, emphasizing their potential clinical applications.

背景：银屑病（Psoriasis）是一种以多因素发病机制为特征的慢性免疫介导性炎症性皮肤病。近期研究广泛证实了银屑病与多种炎症标志物间的强相关性，此类标志物被视为评估全身炎症状态的新型预测工具。
方法：本研究分析了美国国家健康与营养调查（NHANES）的横断面数据。为评估模型性能与泛化能力，数据集被随机划分为70%的训练集与30%的验证集。为解决训练数据中的类别不平衡问题，本研究采用混合重采样技术（SMOTEENN）进行处理。随后，基于预处理后的训练集构建了9种分类算法，包括随机森林、神经网络、XGBoost、k近邻（k-nearest neighbors）、梯度提升树、逻辑回归、朴素贝叶斯（naïve Bayes）、AdaBoost以及支持向量机（SVMs）。最终的梯度提升模型通过R语言的gbm包实现，其超参数选自caret框架的默认调优网格。基于表现最优模型的预测结果，筛选出分类效用最高的炎症生物标志物。
结果：最终分析共纳入22908名研究参与者。梯度提升模型（受试者工作特征曲线下面积（AUC）：0.629，95%置信区间（95% CI）：0.588–0.669）表现最优，紧随其后的是逻辑回归模型（AUC：0.627，95% CI：0.588–0.666）。在所有检测的炎症标志物中，单核细胞淋巴细胞比值（MLR）的分类性能最佳，AUC值为0.662（95% CI：0.640–0.683），其次是中性粒细胞-单核细胞淋巴细胞比值（NLMR），其AUC值为0.661（95% CI：0.640–0.683）。其余标志物包括中性粒细胞淋巴细胞比值（NLR）、动态中性粒细胞淋巴细胞比值（dNLR）、系统免疫炎症指数（SII）、系统性炎症反应指数（SIRI）以及血小板淋巴细胞比值（PLR），其AUC值介于0.658至0.661之间。MLR的相对重要性得分最高，表明其在银屑病分类模型的预测性能中发挥关键作用。NLR位列第二，其次是SII与SIRI（贡献度中等），而PLR的贡献度最低。
结论：在所有测试的算法中，梯度提升模型表现最佳。该模型不仅具备最高的预测准确率，同时在分类效能与特征重要性分析方面表现优异，明确了MLR、SII与NLR等关键炎症标志物。此类标志物可作为评估全身炎症状态与预测银屑病发生发展的可靠指标，凸显了其潜在的临床应用价值。