# Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural Network
###### Abstract  
Cloud computing plays a vital role in healthcare as it can store a large amount of data known as big data. In the current emerging era of computing technology, big data analysis and prediction is a challenging task in the healthcare industry. Healthcare data are very crucial for the patient as well as for the respective healthcare services provider. Several healthcare industries adopted cloud computing for data storage and analysis. Incredible progress has been achieved in making combined health records available to data scientists and clinicians for healthcare research. However, big data in health cloud informatics demand more robust and scalable solutions to accurately analyze it. The increasing number of patients is putting high pressure on healthcare services worldwide. At this stage, fast, accurate, and early clinical assessment of the disease severity is vital. Predicting mortality among patients with a variety of symptoms and complications is difficult, resulting inaccurate and slow prediction of the disease. This article presents a deep learning based model for the prediction of patient mortality using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. Different parameters are used to analyze the proposed model, i.e., accuracy, F1 score, recall, precision, and execution time. The results obtained are compared with state-of-the-art models to test and validate the proposed model. Moreover, this research suggests a simple and operable decision rule to quickly predict patients at the highest risk, allowing them to be prioritized and potentially reducing the mortality rate.  
machine learning; deep neural network; mortality prediction; accuracy; e-health; cloud computing +
Footnote †: journal: Medical Informatics  
way [7]. The development of such systems can lead to self-organized networks that leads to service continuity in distributed systems. Realtime data analysis can be used to trigger different events and lead to Service Oriented Networks (SONs) [8].  
This research presents an efficient framework for the prediction of mortality rate for cloud health informatics. The study aims to provide crucial information to the physician at an early stage so that the physician can understand the critical condition of the patient and can take timely action. The required data are dynamically accessed and updated from the electronic health care record with short intervals. Machine learning algorithms are used for mortality prediction. For this purpose, different machine learning methods are employed, including Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), Support Vector Machine (SVM), Neural Network (NN), and Gradient Boosting (GB). MIMIC III data are used for training and testing of the proposed model. The models are evaluated using different parameters including accuracy, precision, recall, F-Score, and execution time. The deep neural network model with coefficient correlation matrix yields better results as compared to other models used.  
The paper is organized as follows. Section 2 presents literature review with a detailed discussion of state-of-the-art methods. Section 3 presents materials and methods with a detailed discussion of the dataset, features selection, and other steps of the proposed algorithm. Section 4 presents results and discussion, followed by the conclusion section.