## 2 Review of Existing Work
The classification of data has been carried out by using SVM [45] (Support Vector Machine with polynomial kernels and RBF), LR (Logistic Regression) [46], DT (Decision Tree) [47] and KNN (K-Nearest Neighbour) [48]. The suggested ML model [49] has detected the risk of UTI with a recall value of 85% and precision of 86% with the help of in-home monitoring data. In the existing study, UTI in pregnant women and children less than age 10 has been considered. ML approach has tested three algorithms such as RF [50], NN [51] and extreme gradient boosting; at the same time, factoring of independent variables has been executed. A sensitivity of 95% has been achieved by using the suggested model [52]. Similarly, the existing model [53] has aimed to develop an ML model that predicts the risk of MDR UTI (Multidrug Resistant Urinary Tract Infection) for patients after hospitalization. Catboost has been used to exhibit the results, and the outcomes are found to be 0.909 as Matthews's correlation coefficient (MCC), 0.809 as f1-score and 0.717 as accuracy. The predictive model has used an ML algorithm and provided an efficient performance that facilitates UTI. The suggested model [54] has predicted the RUTI (Recurrent Urinary Tract Infection) that has been associated with vesico-ureteral reflux and with a value of 0.761 as the AUC (Area Under the Curve) in the test set. ML algorithms have been used in the prediction of identifying the incidence of stroke and post-stroke prognosis for immobile stroke patients. The ensemble learning model in the recommended study has provided an efficient performance in the prediction of UTI risk. A Knowledge Discovery and Data Mining (KDDM) approach using ML methods has been implemented to find the knowledge regarding HA-CAUTI (Hospital-Acquired Catheter-Associated Urinary Tract Infection) from various sources of data and helps in predicting the risk of HA-CAUTI. ML techniques such as DT, LR and SVM have been described in the existing study [55]. The cutting-edge ML approach has been introduced in electronic health records, which improves secondary utilization. The accuracy of DT, SVM and LR has been found to be 75.87%, 71.50% and 75.83%, respectively. The test characteristics of ML models have been evaluated to find the results of SBI (Serious Bacterial Infections) in infants and the models have been compared with the recognised clinical prediction rules. The four supervising models such as single-hidden layer neural network, LR, SVM and RF have been developed and validated. The methods have been used for the exact identification of patients without UTI and the recommended model [56] has attained a sensitivity of 98%. Statistical models have utilized EHR (Electronic Health Record) for identifying the complications of surgery such as UTI, sepsis, bleeding and surgical infections. The elastic-net model has been used to calculate the regression coefficient and to process variable selection. The recommended model [57] has achieved 83% of sensitivity, 88% of specificity, an area under the curve of 0.93, 52% of positive predictive value and 97% of negative predictive value. In order to improve predictive accuracy, alternative ML approaches (DL methods and tree-based methods) have been implemented and explored in the recommended study [58]. To predict the risk of an AE (Adverse Effect) including UTI, the selection operator regulation method, least absolute shrinkage and LR have been applied. For overall AE, the AUC of the prediction model has been found to be 0.7. However, numerous studies have shown AUC values between 0.6 and 0.78 in predicting the complications of UTI after spine surgery. Initially, ML methods have trained both huge records of patients and measurements of antibiotic resistance. The existing study [59] has provided a way to use ML in decision-making, thereby it helps in the recommendation of antibiotics over UTIs, which has reduced the risk of failure of treatment. By using Fourier transform infrared, bacterial susceptibility has been examined for first-culture _E.