Predictive modeling of co-infection in lupus nephritis using multiple machine learning algorithms

Abstract

Abstract This study aimed to analyze peripheral blood lymphocyte subsets in lupus nephritis (LN) patients and use machine learning (ML) methods to establish an effective algorithm for predicting co-infection in LN. This study included 111 non-infected LN patients, 72 infected LN patients, and 206 healthy controls (HCs). Patient information, infection characteristics, medication, and laboratory indexes were recorded. Eight ML methods were compared to establish a model through a training group and verify the results in a test group. We trained the ML models, including Logistic Regression, Decision Tree, K-Nearest Neighbors, Support Vector Machine, Multi-Layer Perceptron, Random Forest, Ada boost, Extreme Gradient Boosting (XGB), and further evaluated potential predictors of infection. Infected LN patients had significantly decreased levels of T, B, helper T, suppressor T, and natural killer cells compared to non-infected LN patients and HCs. The number of regulatory T cells (Tregs) in LN patients was significantly lower than in HCs, with infected patients having the lowest Tregs count. Among the ML algorithms, XGB demonstrated the highest accuracy and precision for predicting LN infections. The innate and adaptive immune systems are disrupted in LN patients, and monitoring lymphocyte subsets can help prevent and treat infections. The XGB algorithm was recommended for predicting co-infection in LN.

Keywords

• Lupus nephritis
• Infection
• Machine learning
• Lymphocyte subpopulations