# Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural Network
## 2 Literature Review  
Machine learning techniques are successfully employed in many domains, including network security [9], resource utilization prediction [10], data compression [11], healthcare [12; 13; 14], and many more. There exist review articles covering the use of machine learning in healthcare and other applications [15; 16; 17]. Some articles also cover feature selection and feature dimensionality reduction, and weighting [18; 19; 20; 21]. This section presents more related works to the proposed model. A comparative analysis of different methods is also presented. Different techniques have been developed and used for predicting the risk of ICU mortality in the past decade. ICU is a critical care unit that is equipped with advanced technology medical machinery for monitoring patients continuously. The increasing need for these electronic devices creates an opportunity for the approaches of machine learning for the prediction of mortality. Authors in [22] used machine learning and cloud computing to develop a system for monitoring the health status of heart patients. Different machine learning models are evaluated to obtain the most accurate model while targeting the Quality of Service (QoS) parameters. To validate the selected model, cross-validation methods are used using different parameters, including accuracy, sensitivity, specificity, latency, memory usage, and execution time. The proposed algorithm along with a mobile application is also tested on cloud platform with different input datasets. A lightweight authentication protocol for IoT devices in cloud computing environments is proposed in [23]. The proposed protocol consists of three layers, i.e., IoT devices, trust center at the edge layer, and cloud datacenter. The trust center is used to authenticate and authorize the device to access the services provided by the cloud service provider. An algorithm for the prediction of mortality of heart failure is proposed in [24]. Various machine learning models are applied to heart disease datasets to predict the mortality of patients from heart failure. Different performance measures, including accuracy, precession, and recall, are used to compare different machine learning models. Ensemble learning is used to predict mortality based on the accuracy achieved during the validation. Authors in [25] use a deep reinforcement learning approach for computation offloading and auto-scaling in mobile fog computing. In the proposed approach, first, a greedy approach is used to evaluate the fog device locally. In the next step, deep reinforcement learning is used to find the best destination for the execution of the module. The approach considers the trade-off between power consumption and execution time during the module selection phase. Comparative experimental results are presented to validate the proposed approach. Another model for heart disease prediction with the integration of IoT and Fog computing using cascaded deep learning model is presented in [26]. In the initial phase, data are collected from the patients using hardware components. In the next phase, features related to heart diseases are extracted. The selected features are then used to predict the disease using a cascaded convolutional neural network. Galactic swarm optimization is used to optimize the hyperparameters. Comparative experimental results are shown to validate the proposed model. Authors in [27] used different machine learning models to predict patient mortality in the ICU. The MIMIC-III dataset is used to develop machine learning models. The authors employed RM, the Least Absolute Shrinkage and Selection Operator (LASSO), LR, and GB for prediction. The performance of the model is evaluated with different parameters.An efficient CNN-based model for the prediction of coronary heart disease is presented in [28]. The proposed model targets the problem of data imbalance to achieve accurate results.