Abstract

Urinary Tract Infections (UTIs) are a prevalent medical condition affecting male felines that can lead to severe discomfort, behavioural changes, and even fatality if not promptly diagnosed and treated. This paper aims to address the limitations of traditional diagnostic methods by integrating Internet of Things (IoT) sensors with machine learning algorithms to predict UTIs in male felines. The study utilizes a multi-modal sensor array to continuously monitor various physiological and behavioural parameters, such as acidity of urine pH levels, heart rate, territorial marking, and eating habits. Observations were categorized into several states, ranging from normal conditions to severe abnormalities, including death. A machine learning model was trained on the collected data to identify early signs of UTIs. The model demonstrated a high predictive accuracy in identifying urinary tract infections before the manifestation of severe symptoms, thus providing a promising avenue for early intervention. The integrated system offers a non-invasive, real-time monitoring solution that could significantly improve the management and the treatment outcomes of UTIs in male feline.