An Intelligent UTI Forecast Model in Fog Empowered Environment Using Regularized XGBoost Ensemble Approach in Quantum Computing

Urinary tract infections offer a substantial risk to renal health, making it imperative to detect and treat kidney infections at an early stage in order to avoid consequences. This research presents an intelligent system that was developed for the purpose of predicting urinary tract infections at an earlier stage. A Regularized Ensemble XGBoost model technique is utilized by the framework for the purpose of data encoding and pathogenic risk factor calculations on the Quantum computation platform. Furthermore, the framework makes use of Internet of Things-based sensors for the purpose of data collection. In the subsequent step, the results of the analysis, along with the health information of the users, are saved in a cloud archive for future reference. For the purpose of validating the system's performance, extensive experiments were carried out through the utilization of real-time patient data.