Machine learning approaches for detection of urinary tract infections

Abstract: Urinary tract infections (UTI) are a common bacterial infection. Diagnosing UTI can be done through urine culturing. While precise, culturing is both time and money consuming. Flow cytometry analysis (FCA) is a different technique that can calculate different attributes in a urine sample. This is both faster and cheaper. Though, the problem with FCA is that it cannot reliably diagnose patients. The aim of this thesis is to investigate how different screening methods perform when applied before culturing. The screening methods uses FCA and some general characteristics to predict UTI. Using machine learning algorithms, different screening methods were compared. The methods were altered using a sensitivity correction such that the sensitivity exceeded 95%. The performance was measured using obtained real life data consisting of 1316 samples and cross validation. The best savings achieved was obtained using random forest. It managed to save up to 46% of the load on the culturing process while keeping a sensitivity of 95.15%. The specificity were 72%. Even though the data set obtained was too small to reliable declare the real performance, the savings looks really promising.