## 3 Proposed Methodology
### Classification of MANN-AM (Modified Artificial Neural Network with Attention Mechanism)
The dataset has been taken from the IoT-based fog environment. The dataset has been collected from [[https://figshare.com/articles/dataset/Predicting_urinary_tract_infections_in_the_emergency_department_with_machine_learning/59594177](https://figshare.com/articles/dataset/Predicting_urinary_tract_infections_in_the_emergency_department_with_machine_learning/59594177) file=10679215]([https://figshare.com/articles/dataset/Predicting_urinary_tract_infections_in_the_emergency_department_with_machine_learning/59594177](https://figshare.com/articles/dataset/Predicting_urinary_tract_infections_in_the_emergency_department_with_machine_learning/59594177) file=10679215) (accessed on 8 March 2018). The dataset consists of 80,387 attributes and 219 rows, and some of them are given below:

**Age:** Shows the patient's age. **Sex:** Represents the patient's gender. **Diabetes:** The presence of diabetes is shown by using a binary output. **Hypertension:** This shows the existence of hypertension by using a binary variable. **UTI history:** This shows the presence of UTI or not. **Fever:** A binary variable shows whether the patient has a fever or not. **Dysuria:** A binary output indicates whether the patient has any discomfort or pain during urination. **Urgency:** A binary output indicates whether the patient feels the need to urinate urgently or not. **Frequency:** A binary output that denotes whether the frequency of the urination is increased over normal times. **Hematuria:** A binary output that denotes whether the patient has blood in their urine or not. **WBC_count:** This represents the count of the white blood cells, which is measured in cells per microliter. **Bacteria_count:** This represents the bacteria count present in the urine of the patient, which is measured in colony-forming units per milliliter. **Nitrite:** A binary output that denotes the existence of nitrite content in a patient's urine. **Leukocyte:** A binary output that denotes the existence of leukocytes in a patient's urine. **Diagnosis:** A binary output that indicates whether the patient has UTI or not. The above-mentioned attributes are some of the attributes that help in training and testing the ML algorithms in predicting the UTIs by using the medical history and symptoms. Pre-processing has been carried out to identify and handle the missing values. Categorical data have been encoded with the values 0 and 1 and check for the missing values. ### Performance Metrics

The performance metrics have become a part of each ML. Metrics, namely, precision, accuracy, f1-score, specificity, recall and sensitivity are utilized to analyse the classification models. ### Experimental Results

The experimental results attained when using the proposed method MANN-AM have been shown in Figure 3, in which the red color denotes 0 and the blue color denotes 1. The green line denotes the target value that classifies the target data. Figure 4 shows the feature importance of the proposed model based on the number of iterations. From Figure 4, it is clearly found that during the initial iteration, the proposed model has taken a long time for feature selection. After several iterations, the proposed model has learned to select features more efficiently. Selecting the appropriate features is an important step for predicting UTI. Based on the selected features, the proposed model performs a classification by using MANN-AM, which efficiently prognosticates the existence/absence of UTI. Figure 3: ANN display model. ### Performance Analysis

Performance analysis of the proposed study that has used the proposed MANN-AM classifier, KNN, NB and RF has been discussed; it gives detailed information and helps in a better understanding of the proposed model. Figure 5 shows the AUC curve for the proposed MANN-AM classifier, Naive Bayes, K-Nearest Neighbour and Random Forest, where TPR refers to True Positive Rate and FPR refers to False Positive Rate. AUC shows a collective measure of performance across all possible classifications.