## 5 Results and Discussion  
### Model Construction  
As mentioned previously, we developed models to predict the severity level of dengue with ANN and DA. The models will be able to predict the severity level of dengue based on the laboratory test results of the corresponding patients. The accuracy of both models developed with ANN and DA would be evaluated, and the one with the higher accuracy would be the proposed model. For short, we mentioned the models as \"predictive models\" in Figure 1.  
By inputting the values of hematocrit, hemoglobin, platelet count, WBC, monocyte, lymphocyte, and neutrophil of the corresponding patient into the predictive model, the model would be able to process those values and predict the severity level of the dengue patient. In other words, the input variables of the model were the independent variables of this research, which were the seven laboratory test parameters. While the output variable was the dependent variable of this research, which was the severity level of dengue. Before we developed the model, we conducted [PERSON]'s rank correlation test. This correlation test was conducted for two purposes:  
* To examine whether there is a significant correlation between the independent variables. To be able to use DA, there has to be no multicollinearities between the independent variables. This means all of the independent variables are not correlated. If there's a couple of correlated independent variables, one of them has to be eliminated. * To conduct feature selection. This means to select only the necessary independent variables to develop the models. First, we conducted a two-ways hypothesis test, where the hypotheses were as follows:  
Figure 1: Experiment workflow. H\({}_{0}\): \(\rho=0\), which means there is no significant correlation between variable x\({}_{\rm i}\) and x\({}_{\rm j}\), where i, j = 1,..., 7, i \(\
eq\) j. H\({}_{1}\): \(\rho\
eq 0\), which means there is a significant correlation between variable x\({}_{\rm i}\) and x\({}_{\rm j}\). We used a significance level of 0.05. Below were the results of the correlation test. The elements of Table 2 were the correlation coefficients between the corresponding independent variables. The starred coefficients mean that there is a significant correlation between the corresponding independent variables, or in other words, the corresponding independent variables were correlated. The correlated independent variables were as follows:  
* Hemoglobin and hematocrit
* Hemoglobin and platelet count
* Hematocrit and platelet count
* WBC and platelet count
* Neutrophil and lymphocyte
* Neutrophil and monocyte  
Therefore, we eliminated some of the independent variables which were correlated with more than one other independent variables. The eliminated independent variables were hematocrit, platelet count, and neutrophil. Furthermore, we only used the remaining four independent variables to develop the models - hemoglobin, WBC, lymphocyte, and monocyte. Then, we applied the upSample technique because the data that had been used in this research was imbalanced. upSampling means doing a sampling with replacement, in a random manner, to the data in the minority category until the sample size equals the size of the majority category. upSample is one of the techniques to handle imbalanced dataset. An imbalanced dataset is usually upSampled before it is used to develop the model, so the developed model will tend to have higher accuracy. If the dataset is not upSampled and is directly used to develop a model, it is unlikely to yield a model with high accuracy. The dataset used in our research was imbalanced, in the sense that the classes are not represented equally. Before the data was upSampled, it consisted of 77 cases. From the 77 cases, 38 cases were from category DF, 28 cases were from category DHF grade 1, and 11 cases were from category DHF grade 2. It is clear that the majority category was DF with a size of 38, and the minority category was DHF grade 2 with size 11.