A Novel IoT-based Framework for Urine Infection Detection and Prediction using Ensemble Bagging Decision Tree Classifier

One of the most common conditions treated in adult primary care medicine is Urinary Tract Infection (UTI), which accounts for a sizeable portion antibiotic prescriptions. A high degree diagnostic accuracy necessary because this issue so prevalent and important everyday clinical practice. Particularly light rising prevalence resistance, excessive prescriptions should be avoided. To examine machine learning approach Internet Things (IoT) urinary tract infections, research proposes an Ensemble Bagging Decision Tree Classifier (EBDTC). In our study, to learn more about UTI, we conducted study collected physiological data 399 patients preprocessed them using min-max scalar normalization. Feature extraction Principle Component Analysis (PCA) classification The performance outcomes (96.25%), precision(96.22%), recall (98.07%), f-1 measure(97.17%) demonstrate proposed strategy's significantly improved comparison other baseline existing techniques.