Advancing Early Diagnosis of APD-Related Infections Through Interpretable Machine Learning

APD is widely adopted in the management of end-stage renal disease (ESRD) and offers flexi-bility and improved quality of life, but bacterial infections, particularly peritonitis, are still a major constraint, which frequently results in hospitalization, catheter failure, and hemodialysis. Early diagnosis is important but difficult because of the non-specific clinical manifestations and delays related to the traditional diagnostic techniques like culture-based analysis. “To overcome these restrictions, this paper suggests a new explainable machine learning model to early identify bacterial infections in APD patients based on multimodal data streams, such as clinical, lab, and time-series dialysis data, to identify both fixed and dynamic infection onset patterns”. The framework uses a hybrid characteristic of feature engineering, which is a combination of statistical selection techniques and clinically relevant indicators to improve predictive performance, and Supervised learning models of high accuracy like the Random Forest, SVM, and Gradient Boosting are applied. One of the contributions of this work is the incorporation of explainable artificial intelligence through SHAP that leads to a clear interpretation of model predictions and the determination of key risk factors that will affect the development of the infection and thus enhance clinical trust and usability. The experimental findings indicate that the given approach greatly enhances the accuracy of early detection as compared to the conventional ones, allowing timely intervention, minimizing complications, and improving the overall outcomes of the treatment, which underscores its potential as a scalable and clinically applicable decision support system to manage APD.