# Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural Network
## 5 Results and Discussion
### Evaluation Measures  
Evaluation metrics are used to evaluate the performance of the prediction model. Accuracy, precision, recall, and F-score are used to evaluate the performance of different models. The term accuracy refers to how much close the produced results are to the true values. For uniform datasets it is a useful evaluation parameter. Accuracy is calculated using Equation (2).  
\[\text{Accuracy}=\frac{\text{TP}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}} \tag{2}\]  
Precision shows how close two or more quantities are to each other. High precision is associated with a low false-positive rate. Precision is calculated with Equation (3).  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{3}\]  
Recall is the division of the related data that are retrieved successfully. It is the number of results that are correct and divided by the number of results that should be returned, as shown in Equation (4).  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{4}\]  
F-score is a measure of the accuracy of a model on a dataset. It is calculated as the normalized average of precision and recall including both false positives and false negatives. F-score is calculated as shown in Equation (5).  
\[\text{F}-\text{Score}=2\times\frac{\text{PR}}{(\text{P}+\text{R})} \tag{5}\]  
Comparative results are presented in Table 2. Results analysis show that DNN outperforms the other frameworks by achieving accuracy of 80% followed by gradient boosting with 78% accuracy. The DT gives the lowest accuracy rate of 70% on the extracted features of the dataset used. In terms of precession, DNN achieved a precision of 86% which is higher as compared to other algorithms. DNN is followed by gradient boosting with 80% precision. While the decision tree gives the lowest precision of 72%. DNN achieved recall score of 79% followed by gradient boosting with a 78% recall score. While decision tree and SVM linear give the lowest recall score of 70%. In terms of F1-score, DNN achieved an F1-score of 80% followed by gradient boosting with a 78% F1-score. While LR and SVM linear give the lowest F1-score of 70% on the extracted features of the dataset used.  
According to the results shown in Table 2, DNN obtained 6.67% improvement over RR and SVM-linear, 14.28% improvement over DT, 5.26% improvement gain over SVM-rfb and RF, 3.89% improvement over NN, and 2.56% improvement gain over GB in terms of accuracy. The results show that in terms of precision, DNN obtained 17.18, 19.44, 13.25, 10.25, and 7.5 percent improvement gain in comparison to DT, LR, SVM-linear, SVM-rfb, RF, NN, and GB, respectively. The results show that the percent improvement gain attained by DNN in terms of recall over LR is 2.59 and 12.89 percent over SVM linear. The improvement gain over both SVM-rfb and RF is 5.33 percent. In comparison to GB and NN, DNN attained improvement gain of 3.94 percent. In comparison to SVM-linear and DT, the proposed framework yields 12.85 percent improvement gain. The resultsanalysis shows that DNN obtained 5.26 percent improvement over LR, SVM linear, and NN. DNN yields 2.56 percent improvement over DT, 3.89 percent improvement over SVM-rfb, and 6.66 percent improvement gain over RF in terms of F1-score. The objective of the proposed study is to predict mortality using machine learning techniques. All these methods are parametric, i.e., based on probability distribution that ensures that the results are statistically significant.