Survey on Machine Learning Models to Analyze Urinary Tract Infection Data

A urinary tract infection, or UTI, is caused when bacteria get into the urinary tract- kidneys, bladder, or urethra. UTIs cause more than 8.1 million visits to healthcare providers each year. About 60% of women and 12% of men get infected with UTI during their lifetime, therefore being more prominent in females. UTIs can be found by analyzing a urine sample. The urine is examined under a microscope for bacteria or white blood cells that show infection. Healthcare providers may also take a urine culture. This test examines urine to detect and identify bacteria and yeast, which may be causing a UTI. Several models have been proposed to predict urine culture positivity based on urinalysis. Applications of machine learning (ML) methods have been used extensively to solve various complex challenges in recent years in various application areas. ML methods are characterized by their ability to examine data and discover exciting relationships, provide interpretation, and identify patterns. ML can help enhance the reliability, performance, predictability, and accuracy of diagnostic systems for many diseases. This survey provides a comprehensive review of the use of ML in the diagnosis of Urinary Tract infection in human beings. To provide a reliable classification of results assistance of 27 algorithms was tested. Algorithms applied included Logistic regression, Decision tree, Random Forest, and Support Vector Machine. Each model was evaluated by F1-score, AUC-ROC, accuracy, sensitivity, and specificity. Baseline epidemiological factors, previous antimicrobial consumption, medical history, and previous culture results were included as features. Machine learning models such as the Artificial neural network have been used as well for the prediction of the presence of urinary infection. Some specific parameters have been selected with the help of the Analysis of variance technique which gave high accuracy. This survey provides a comprehensive review of the use of ML in the medical field highlighting standard technologies and how they affect medical diagnosis. It provides valuable references and guidance for researchers, practitioners, and decision-makers framing future research and development directions. It is found that Machine Learning models can improve the early prediction of urine culture positivity and UTI by combining automated urinalysis with other clinical information. Clinical utilization of the models can reduce the risk of delayed antimicrobial therapy in patients with nonspecific symptoms of UTI and classify patients with UTI who require further treatment and close monitoring. In conclusion, the paper provides a survey on the machine learning models used with the highest accuracy to detect UTI potential patients.