## 2 Review of Existing Work  
In recent times, the application of smart healthcare attains more popularity. Various ML methods have been used in UTI for feature selection and classification. The section has reviewed the conventional models briefly along with the gaps identified from the state-of-the-art works. IoT in fog environments has been used for quick prediction and diagnosis of urine infection. Initially, multiple sensors have been used to deliver the data and by using XGBoost [27] ensemble a learning approach, IRF (Infectious Risk Factor) has been calculated at the fog layer. The suggested study [28] used real-time patient data. The results of accuracy, f-score, sensitivity, precision and specificity have been given as 91.45%, 90.12%, 84.79%, 95.49% and 95.96%, respectively. For monitoring and prediction of UTIs, the existing paper [29] has suggested an IoT-inspired framework in a home-centric environment. By using ID (Infection Degree), probabilistic classification [30] (Bayesian Probabilistic Model) of UTI has been performed in accordance with both infection and non-infection categories. Temporal-Artificial Neural Network (t-ANN) technique, which incorporates IIM (Infection Index Measure), has been used for the detection and prediction of UI. Kohonen Self Organized Mapping Technique [31] (SOM) has been used for the color visualization of information. Additionally, the development of AI has brought several improvements in the healthcare sector. Thereby, ML and DL methods have also been widely used in detecting several life-threatening diseases such as cervical cancer [32] and COVID-19 [33; 34] and also in predicting PCOS [35]. Diagnosis and monitoring of clinical voiding dysfunction have been assisted in the recommended study [36]. While urinating, the vibration signals have been detected with the help of an accelerometer and they transformed into MFCC (Mel-Frequency Cepstrum Coefficient) [37]. The CNN model with pooling and normalization layers with ReLU (Rectified Linear Unit) [38] has acted as active functional layers. Classification has been done through the Softmax function [39]. UMAP (Uniform Manifold Approximation and Projection) [40] dimensionality reduction has been used to predict and analyse uroflowmetry and also helps in void dysfunction diagnosis. The result of accuracy has been found to be 98%. An integrated platform has been developed and ML models have been used for the analysis of UTI risk and agitation in the suggested study. A huge dataset has been collected, by which a new DL (Deep Learning) model has been evaluated that utilized rational and attention mechanisms. Rational layers have passed through the LSTM layer, and LSTM [41] has processed the data available in the attention block. For prediction, classifiers have used time steps. In detecting the risk of UTIs and agitation, the outcome values of the suggested model [42] for precision and recall have been given as 83% and 91%. Noise, explainability, high dimensionality, large variability and label scarcity are some challenges present in anomaly detection. The efficiency of MP (Matrix Profile) and SOTA has improved by using CMP (Contextual Matrix Profile), which interprets and visualizes the anomalies present in the data of the noisy real world. Graph-based anomaly detection has been used to detect anomalies by using a real-world dataset in healthcare. The recommended method [43] has possessed an enhanced alert rate, recall and generalizability. Adaptive DNN (Deep Neural Network) [44] and supervised parabolic model have trained the model of unlabelled data by utilizing unsupervised techniques. DNN has utilized auto-encoder in terms of learning unlabelled data representation. The trained model has been applied to the labelled data of a smaller set, which helps in the development of a supervised classifier with improved reliability. DL and probabilistic models have been used to analyse UTI risks.