1. ANISHA - Computer Science Engineering Department, Galgotias University, Greater Noida, India.
2. MUNISH SABHARWAL - Computer Science Engineering Department, Galgotias University, Greater Noida, India.
3. ROHIT TRIPATHI - Electronics Engineering Department, J C Bose University of Science & Technology, YMCA, Faridabad, India.
Urinary tract infection (UTI) is common infection in humans due to several reasons. It occurs with the incursion and growth of wide range microorganisms, consists of bacteria such as Gram-negative, Grampositive and fungi. The urinary system consists of kidneys, urethra, ureters and bladder which get affected by urinary tract infection. Primarily the infection occurs in the lower tract of the urinary system i.e. bladder and urethra. UTI get severe, if it appears in upper tract which includes kidneys and ureters. UTI is among a most prevalent diagnosed infection that affects both men and women and it is more common in women because of their physiology. UTI give rise to several diseases related to liver, kidneys and bladder if it left untreated. In the last few years, numerous healthcare devices have emerged which permits people to detect and manage their health conditions easily at home. The aim of this paper is to review and assess the causes, impacts, requirements for the prediction of urinary illness. Urine infection (UI) is the highly common disease in the world's population. UI monitoring has been an issue of concern for healthcare industry. Outcomes are equated by several state-of-the-art forecast methods that shows the reviewed method attain major improvement and high efficacy. Internet of Things enhanced responses in terms of efficiency classification, forecast productivity, temporal delay and stability.
Urinary tract infection (UTI); IoT; artificial neural network (ANN); recurrent neural network (RNN).