## 3 Proposed Methodology
### Classification of MANN-AM (Modified Artificial Neural Network with Attention Mechanism)
```
Input : Input : dataset \(\mathrm{df},x_{\mathrm{train}},x_{\mathrm{test}}-\) independent variable, \(y_{\mathrm{train}},y_{\mathrm{test}}\)target variable Output : target \(y_{\mathrm{preds}}\) with tract infection of UTX_DIAGNOSIS\((0,1)\) scale the datasets using // input layer divide \(x_{\mathrm{train}}x_{\mathrm{test}}\) and \(y_{\mathrm{train}},y_{\mathrm{test}}\)//input layer W is calculated based on dist using //hidden layer set value for parameter k Estimating the distance between train and test Sorting dist in an ascending pattern select the best \(k_{\mathrm{neigh}}\) Repeating steps 2-4 until the algorithm is over W matrix is saved as result Simulation involves using //hidden layer Saving results Retrieving ANN with attention model Setting values for number of input, output and hidden layers Primary weighing of existing neurons in input, output and hidden layers Calculating the output (y) for each neuron in output layer Updating ANN parameters Repeating steps 3-4 until the algorithm is over Saving results End of hybrid model Displaying results EndThe RF algorithm is shown in Algorithm 3. ```
Input : dataset \(\mathrm{df},\mathrm{x}_{\mathrm{train}},\mathrm{x}_{\mathrm{test}}-\mathrm{ independent variable},\mathrm{y}_{\mathrm{train}},\mathrm{y}_{\mathrm{test}}\)target variable output : target \(\mathrm{y}_{\mathrm{preds}}\) with tract infection of UTX_DIAGONSIS A is training set \(\mathrm{TS}:=(\mathrm{a}_{1},\mathrm{b}_{1}\ldots\ldots,(\mathrm{a}_{n} \mathrm{b}_{n})\) training \(\mathrm{set}(\mathrm{TS})=\mathrm{images}\) F - feature, \(\mathrm{N}(\mathrm{t})=\mathrm{nooftrees}\) Forest = E begin function \(\mathrm{RandomForest}(\mathrm{H},\mathrm{R})\) I \(\leftarrow\emptyset\) for \(\mathrm{i}\mathrm{e}\mathrm{I},\ldots\mathrm{E}\) do \(\mathrm{H}^{(\mathrm{i})}\leftarrow\mathrm{A}\) bootstrap sample from TS \(\mathrm{a}_{i}\leftarrow\mathrm{Randomized}\) Tree Learn \((\mathrm{H}^{(\mathrm{i})},\mathrm{R})\) I \(\leftarrow\mathrm{I}\cup(\mathrm{a}_{i})\) end for return I end function \(\mathrm{RandomizedTreeLear}(\mathrm{H},\mathrm{R})\) At each node : F \(\leftarrow\) very small subset of R split on best feature in F Return the learned tree end function End for End
```
**Algorithm 3** Random Forest

The overall algorithm of K-NN is given in Algorithm 4. ```
Input : dataset \(\mathrm{df},\mathrm{x}_{\mathrm{train}},\mathrm{x}_{\mathrm{test}}-\mathrm{ independent variable},\mathrm{y}_{\mathrm{train}},\mathrm{y}_{\mathrm{test}}\)targetvariable Input : dataset \(\mathrm{df},\mathrm{x}_{\mathrm{train}},\mathrm{x}_{\mathrm{test}}-\mathrm{ independent variable},\mathrm{y}_{\mathrm{train}},\mathrm{y}_{\mathrm{test}}\)targetvariable output : target \(\mathrm{y}_{\mathrm{preds}}\) with tract infection of UTX_DIAGONSIS begin for each y belong df do  calculate the Dist \(\mathrm{D}(\mathrm{x},\mathrm{y})\)bet y and x end \(\mathrm{subset}(\mathrm{N})\) from the data frame \((\mathrm{df})\) N contains m training samples which are the knn of the test samples x \(\mathrm{categorical}_{\mathrm{variables}}\) of x : \(\mathrm{y}_{\mathrm{x}}=\mathrm{arg}_{\mathrm{y}\mathrm{c}\mathrm{Y}}\mathrm{ max}\sum\limits_{\mathrm{y}\in\mathrm{Y}}\mathrm{I}(\mathrm{y}=\mathrm{class}(\mathrm{categorical}_{\mathrm{variables}}))\) end
```

**Algorithm 4** K-Nearest Neighbour

The NB algorithm is based on the conditional probability principle as per [PERSON] theorem. The following steps are to be followed (as exposed in Algorithm 5). ## 4 Results and Discussion

The results that have been obtained by the implementation of this work are included in this section, with its dataset description, standard-performance metrics, experimental results, performance and comparative analysis. ### Dataset Description

Predictor variables that include laboratory results, urine dipstick results, urinalysis, past medical history, structure historical findings, physical exam findings, chief complaints and demographic information were available in the dataset that includes a reduced set of 10 variables and a full set of 211 variables.