Prediction of Urinary Tract Infection Using Machine Learning Algorithms

The prevalence of urinary tract infections (UTIs) is a significant concern, particularly among women. This study aims to adopt machine learning models for predicting UTIs using gradient boosting, support vector machine (SVM), and random forest algorithms. The dataset used was obtained from the Kaggle repository, consisting of UTI articles. The performance of the models was evaluated using metrics such as accuracy, precision, recall, and F1-score. The UTI dataset was preprocessed by removing irrelevant columns and handling missing values. The dataset was then divided into training and testing sets. The three models were trained using the training data. The results showed that both random forest and gradient boosting classification achieved an accuracy of 0.8750, while SVM achieved an accuracy of 0.8333. Precision values were 0.8000 for random forest, 1.0000 for SVM, and 0.9000 for gradient boosting. F1-scores were 0.8421 for both random forest and SVM, and 0.8571 for gradient boosting. Recall values were 0.8889 for both random forest and SVM, and 0.8182 for gradient boosting. Comparing the results with previous studies, the models showed competitive performance. The ROC curves were plotted to visualize the models' performance across different classification thresholds. In conclusion, the study demonstrates the effectiveness of gradient boosting, SVM, and random forest algorithms in predicting UTIs. The adopted models provide valuable insights for early detection and management of UTIs, contributing to improved healthcare outcomes.