IoT-Enhanced Machine Learning Approach for Early Detection and Prediction of Urinary Tract Infection

Millions of people worldwide suffer from urinary tract infections (UTIs), which are a common and recurring health problem. In order to limit possible health problems and stop the spread of disease, early identification and prompt management are essential. In order to overcome this issue, the Internet of Things and machine learning techniques are combined in the novel approach presented in this study. The suggested method continually monitors real-time vital signs and urine biomarkers using IoT-enabled wearable and smart sensors. Principle Component Analysis feature selection approaches are used to optimize the pertinent data inputs in order to improve the accuracy of UTI prediction. A centralized ML-driven processing unit is used to perform complex ML algorithms, such as the Ensemble Bagging Decision Tree Classifier classification model, to analyses and decipher the patterns of the gathered data. The technology reliably recognizes and anticipates UTIs, sending early warning signals to patients and medical professionals. In order to improve patient care and public health outcomes, our ML-IoT fusion in UTI detection offers a non-intrusive and proactive technique. The suggested EBDTC technique outperforms the existing approaches with an accuracy rate of $\mathbf{9 6. 2 5 \%}$. The results of the study demonstrate how disruptive technology has the ability to successfully treat common health issues, expanding the area of healthcare diagnostics.