Hospital-acquired infections (HAIs) remain a major global concern and contribute significantly to increased morbidity, mortality, and healthcare costs. Among the pathogens that cause Escherichia coli (Escherichia coli) is one of the most frequently isolated microorganisms, especially in urinary tract infections (UTIs), bloodstream infections, and surgical site infections. Early and accurate predictions Escherichia coli Hospitalized patient infections remain a significant clinical challenge, but have the potential to significantly improve patient outcomes. Additionally, identifying patient-related risk factors can support targeted infection control strategies. This study aims to evaluate a no-code machine learning (ML) approach for early prediction. Escherichia coli Investigate the possibility of infection and identify associated risk factors. ML techniques offer a powerful alternative by enabling the analysis of high-dimensional, heterogeneous datasets, facilitating the discovery of hidden patterns, and supporting personalized risk prediction. In this study, a total of 300 clinical samples were collected as a training dataset from hospitalized patients between July 2024 and February 2025 in multiple wards of Zagazig University Hospital in Sharqia, Egypt. An independent internal validation dataset of 100 samples was collected from the same hospital in May 2026. The goal was to assess the generalizability of the model to data we had never seen before. Bacterial isolates were identified using standard biochemical methods. Data analysis was performed using the Orange visual programming platform, implementing a modular ML pipeline that integrates data preprocessing, feature processing, model training, and performance evaluation within a no-code environment. Naive Bayes models show predictive potential Escherichia coli Infections in hospitalized patients. The model is intended to predict Escherichia coli Infection at the time of specimen collection before culture results are finalized, depending on clinical data. However, further validation in a large multicenter prospective cohort is required before clinical implementation.
