Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables

Abstract

Background: Urinary tract infection (UTI) is a frequent health-threatening condition. Early reliable diagnosis of UTI helps to prevent misuse or overuse of antibiotics and hence prevent antibiotic resistance. The gold standard for UTI diagnosis is urine culture which is a time-consuming and also an error prone method. In this regard, complementary methods are demanded. In the recent decade, machine learning strategies that employ mathematical models on a dataset to extract the most informative hidden information are the center of interest for prediction and diagnosis purposes.

Method: In this study, machine learning approaches were used for finding the important variables for a reliable prediction of UTI. Several types of machines including classical and deep learning models were used for this purpose.

Results: Eighteen selected features from urine test, blood test, and demographic data were found as the most informative features. Factors extracted from urine such as WBC, nitrite, leukocyte, clarity, color, blood, bilirubin, urobilinogen, and factors extracted from blood test like mean platelet volume, lymphocyte, glucose, red blood cell distribution width, and potassium, and demographic data such as age, gender and previous use of antibiotics were the determinative factors for UTI prediction. An ensemble combination of XGBoost, decision tree, and light gradient boosting machines with a voting scheme obtained the highest accuracy for UTI prediction (AUC: 88.53 (0.25), accuracy: 85.64 (0.20)%), according to the selected features. Furthermore, the results showed the importance of gender and age for UTI prediction.

Conclusion: This study highlighted the potential of machine learning strategies for UTI prediction.

Keywords: Feature extraction; Machine learning; Prediction; Urinary tract infection.

Fig. 1

Comparison between UTI-infected and non-UTI (non-infected) cases, considering the most informative features

Fig. 2

Correlation matrix of selected variables with categorical values. The color indicates the correlation value and each number indicates the p-value (*: p < 0.05, **: p < 0.01)

Fig. 3

ROC curve for comparison of predictor performance for different age spans. AUC was 86.42 (0.17) (Age: 18–40), 87.71 (0.22) (Age: 40–60), and 89.42 (0.25) (Age > 60), respectively

Fig. 4

ROC curve for comparison of predictor performance according to the gender. AUC was 91.42 (0.32) and 86.16 (0.23) for males and females, respectively