A hybrid feature selection approach for urinary tract infection detection and prediction in IoT-Fog environment

Abstract

Urinary Tract Infections (UTIs) are a prevalent health concern experienced by millions of individuals worldwide and have a significant impact on overall well-being. The clinical presentation of UTIs varies depending on their location and severity. Common symptoms include discomfort during urination, increased frequency, a compelling urge to urinate, discomfort in the lower abdomen, and the presence of blood in the urine (hematuria). Severe cases may be accompanied by fever, pain in the flank region, and other systemic symptoms, indicative of an upper UTI. Precise diagnosis of UTIs is achieved through clinical evaluation and laboratory tests. The primary aim of this research is to utilize appropriate Machine Learning (ML)-based algorithms to predict Urinary Tract Infections (UTIs). The ultimate goal is to develop a predictive model that can be effectively implemented in a smart toilet system. By achieving this objective, this research seeks to provide an innovative and pragmatic solution for UTI prediction, harnessing the potential of ML algorithms in IoT-Fog settings to enhance healthcare and promote public health. This paper introduces a hybrid approach that combines feature selection methods and employs the Guided Regularized Random Forest (GRRF) classification algorithm to aid in the diagnosis of UTIs. A UTI dataset was created using data from routine examinations and definitive diagnostic outcomes for UTI patients. Dimensionality reduction was carried out using Principal Component Analysis (PCA), while feature selection was performed using K-best and Lasso CV techniques. Through the proposed strategy, this study achieved a remarkable accuracy rate of 98.8% and a precision rate of 98.90% in UTI identification. Future UTI prevention and treatment plans must be optimized via further research and ongoing efforts to overcome antibiotic resistance.