# Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural Network
## 6 Conclusions  
Patient mortality prediction is a challenging task due to the huge amount of data and missing values. To achieve better performance and accuracy, we propose a model based on machine learning. In the layer of prediction, we used DNN. We input the data in the pre-processed form given to the model for building an optimum model with hidden layers, neurons, activation functions optimizers, and epochs. The proposed approach highlights certain significant features that may need to be observed further to estimate their effect on mortality of the patient. Various machine learning models are tested, and evaluation metrics are measured. The best performance is obtained from DNN as compared to the other learning algorithms. Overall, DNN performs best and gives the best performance based on measured parameters, i.e., accuracy, precision, recall, and F1-score.  
The execution time of the proposed model is higher as compared to other methods. In future, additional measurement parameters, i.e., time and space complexity need to be considered to justify their significance in the mortality prediction. Moreover, feature selection with other features selection methods can also lead to better results. The model can be tested on other datasets, particularly large-scale datasets to ensure that the model achieves similar results. The proposed model needs to be tested in an IoT-integrated environment to ensure the applicability and validation of the results. The model also needs to be tested in integration with other applications to build a service-oriented architecture for healthcare industry.  
Conceptualization, A.A. and K.T.; methodology, A.A. and M.S.; software, K.T. and M.S.; validation, M.S., M.T., K.T. and A.A.; formal analysis, K.T., M.T. and M.S; investigation, K.T; resources, M.S. and M.T.; data curation, A.A. and M.T.; writing--original draft preparation, K.T.; writing--review and editing, M.S., M.T. and A.A.; visualization, M.T.; supervision, M.T.; project administration, A.A. and M.T.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets used are available publicly.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **Accuracy** & **Precession** & **Recall** & **F1-Value** & **ET (Training)** & **ET (Testing)** \\ \hline LR & 0.75 & 0.73 & 0.77 & 0.76 & 1.89 & 0.33 \\ DT & 0.70 & 0.72 & 0.70 & 0.78 & 1.42 & 0.17 \\ SVM-L & 0.75 & 0.76 & 0.70 & 0.76 & 28.08 & 13.06 \\ SVM-RBF & 0.76 & 0.78 & 0.75 & 0.77 & 33.79 & 16.67 \\ RF & 0.76 & 0.78 & 0.75 & 0.75 & 1.99 & 0.82 \\ NN & 0.77 & 0.78 & 0.76 & 0.76 & 8.06 & 4.33 \\ GB & 0.78 & 0.80 & 0.76 & 0.78 & 1.16 & 0.84 \\ [28] & 0.78 & 0.81 & 0.78 & 0.78 & 31.3 & 14.71 \\ [27] & 0.78 & 0.82 & 0.77 & 0.78 & 25.62 & 13.14 \\ DNN & 0.80 & 0.86 & 0.79 & 0.80 & 44.2 & 22.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of ML algorithms in terms of accuracy, precision, recall, F1 score, and execution time (ET).