## 3 Results
For both decision tree and random forest model training, the settings were chosen by selecting the most accurate settings using the 'grid search' function, which did not make it into the final version of the application due to optimisation considerations. The 'n_estimators' argument specified the number of decision trees; in this case it was 10. The'max_depth' argument specified the depth of the forest; in the case of the forest it was unlimited depth, and in the case of the tree it was a maximum of 20 levels. The'min_samples_leaf' argument specified the number of examples per leaf; for a random forest it was one, while for a tree it was four examples. The'min_samples_split' argument meant how many examples were needed for a node split; for a forest it was two examples, while for a tree it was six examples. The 'criterion' argument indicated which criterion for the quality of the split was used, and in this case it was the Gini criterion. The Neural Network, on the other hand, was trained according to parameters extracted through analysis in Statistica. The 'hidden_layer_sizes' argument specified the number of neurons in each hidden layer of the network. In this case, the hidden network layers had 22 and 49 neurons, respectively. The'solver' argument specified the optimisation algorithm that was used to optimise the network weights. In this case, the optimisation algorithm \"lbfgs\" was used. The'max_iter' argument specified the maximum number of iterations of the optimisation algorithm that were performed by the model. In this case, the algorithm performed a maximum of 148 iterations. The 'activation' argument specified the activation function used by the model. In this case, the activation function 'tanh' was used. The 'random_state'argument was used to ensure reproducibility of the results. This ensured that when the same random state was used, exactly the same model was obtained. The predict function was also a key part. It took input values, and then, using these and the models, predicted the medical supply items. These items were stored as numbers in the csv file, so the number_to_text function converted the numbers into appropriate text describing what eachnumber meant. All results were then displayed on the graphical interface. This function took three parameters: gender, age, and number of weeks. It then performed the following actions: It assigned values to the variables plec, age, and weeks, which werereturned by the functions set_plec(), set_age(), and set_weeks(). These functions returned the values selected in the GUI. A one-dimensional array called input_query was created, which contained the values plec, age, and weeks. The predict() method was called on the model, model2, and model3 objects, which were models of random forest, decision tree, and neural network, respectively. The returned results were assigned to the variables result, result2, and result3. Results were converted from numeric values to text using the number_to_text() function. The times were then shown in the graphical interface to 5 decimal places. The times were followed by accuracy results converted to a percentage in the graphical interface. The predict function then displayed the predicted result on the GUI. The last key section was the graphical interface, which was divided into two parts. The part responsible for the input data started with the initialisation of the 'customkinter' library and the creation of the frame. Next, the application name information at the top of the application was created. The next element was the option field responsible for selecting the gender. The set_plec function was also written for this element, which tookthe selected value and then turned it into a number depending on the selected gender. Then, there were two fields to enter the age and the weeks after the stroke and functions for them to access this data. The last element from this section was the button responsible for the predict function. The second part of the interface was the one responsible for displaying the results.