## 3 Proposed Methodology
### Classification of MANN-AM (Modified Artificial Neural Network with Attention Mechanism)
Table 2 shows the feature ranking by Firefly Algorithm, in which chloride refers to the chloride content that exists in urine, ua_ketones refers to the ketones level in urine, ua_clarity refers to the clarity level of the urine, ua-color refers to the color of the urine, ua-glucose refers to the glucose level in urine, Temp_Mean refers to the body temperature of the patient and the fever attribute shows whether the patient has a fever or not. By using the proposed method, the feature ranking for chloride, HR_Min and chloride again are the three most important features for predicting UTI, and the feature importance scores are given to be 0.089883, 0.074717 and 0.072249, respectively. The other features such as ua_ketones, ua_clarity, ID, ua_color, fever, lang, abd_disted2, ua_glucose, Temp_Mean, HR_First and UCX_abnormal have lower feature importance scores, which indicates that they have a relatively lower contribution in the prediction of UTI. In Table 2, it is observed that chloride has a higher ranking of 0.089883. ### Comparative Analysis

The proposed model has been compared with two traditional studies and the respective results have been discussed. The dataset of the proposed study has used an IoT-based fog environment, hence the existing studies which have similar types of datasets have been considered in the comparative analysis. Table 3 shows the comparative analysis of various models. \begin{table}
\begin{tabular}{c c} \hline
**Feature Importance** & **Feature Ranking by Firefly Algorithm** \\ \hline
**Chloride** & 0.089883 \\ \hline
**HR\_Min** & 0.074717 \\ \hline
**Chloride** & 0.072249 \\ \hline
**ua\_ketones** & 0.033614 \\ \hline
**ua\_clarity** & 0.031833 \\ \hline
**ID** & 0.015908 \\ \hline
**ua\_color** & 0.015579 \\ \hline
**Chloride** & 0.014618 \\ \hline
**fever** & 0.014009 \\ \hline
**lang** & 0.013988 \\ \hline
**abd\_disted2** & 0.01389 \\ \hline
**ua\_glucose** & 0.013608 \\ \hline
**Temp\_Mean** & 0.013354 \\ \hline
**HR\_First** & 0.013126 \\ \hline
**UCX\_abnormal** & 0.013082 \\ \hline \end{tabular}
\end{table} 
Table 2: Feature ranking by Firefly Algorithm. The comparative analysis of performance metrics of the proposed model with the existing study [64] is shown in Figure 12. Table 4 shows the comparative analysis of various models in the existing study [32]. For broad analysis, the comparative analysis has been done with the existing study [32], which has a dataset similar to the proposed study and used various classification models. The comparative analysis of the performance metrics of the proposed model has been shown in Figure 13. In the existing methods, XGBoost has a higher accuracy of 0.9145 compared to other models. In Table 4 and Figure 13, it is revealed that the proposed system has better values in every performance metric and has attained the highest accuracy of 0.99. The proposed MANN-AM classifier has greater potential in focusing on small and significant data. The MFB-FA model for feature selection has helped in attaining a global optimum. Hence, from the experimental results, performance analysis, internal analysis and comparative analysis, it is revealed that the proposed study has better efficacy in every performance metric in detecting UTI. ## 5 Conclusions

This research aimed to identify the existence or absence of UTIs using effective ML-based algorithms. This study used MFB-FA for feature selection and MANN-AM for classification. Significant features chosen by the MFB-FA were also presented to afford an idea about the features selected based on ranking. Experimental results were presented to reveal the outcomes of the proposed system in dividing the target value, thereby classifying the data. To evaluate the performance of this work, an internal comparison was performed with ML algorithms, namely, NB, RF and KNN. The accuracy rate of the proposed system was found to be 0.99, NB exposed 0.65 as the accuracy rate, RF showed 0.85 as the accuracy rate and KNN explored 0.79 as the accuracy rate. From the internal comparison, the