Urinary Tract Infection Detection and Prediction Using Machine Learning and Internet of Things

Abstract

Urinary Tract Infection are a widespread international health issue affecting millions and contributing to repeated symptoms and possible health ailments if left untreated. Early diagnosis and immediate intervention are key to preventing the expansion of infection and reducing the risk of adverse health outcomes. This work presents a novel method of predicting and detecting UTIs by integrating Machine Learning algorithms with the Internet of Things. The system leverages complete urine examination data, including physical findings (e.g., color, clarity, specific gravity, pH), routine, chemical, and microscopic analyses, alongside IoT sensors for real-time monitoring of physical urine properties. The gathered data is analyzed in depth using ML algorithms with cloud computing facilities, which facilitates the interpretation of data patterns. The study employs ML techniques such as Decision Trees, Random Forests, SVM, KNN, and ANNs. The system accurately identifies and forecasts UTI occurrences, transmitting early warnings to patients and healthcare professionals. ML and IoT integration in UTI detection enable a non-invasive and proactive approach that allows for personalized and timely intervention to enhance patient care and promote public health initiatives. This research demonstrates a deployable framework for point-of-care UTI diagnostics, enabling early detection of at-home UTI screening through smart toilet integration or ready-to-use test kits. newline