Cloud computing for ECG analysis using mapreduce

Electrocardiograph (ECG) analysis brings a lot of technical concerns because ECG is one of the tools frequently used in the diagnosis of cardiovascular disease. According to World Health Organization (WHO) statistic in 2012, cardiovascular disease constitutes about 48% of non-communicable deaths worldwide. Although there are many ECG related researches, there is not much efforts in big data computing for ECG analysis which involves dataset more than one gigabyte. ECG files contain graphical data and the size grows as period of data recording gets longer. Big data computing for ECG analysis is critical when many patients are involved. Recently, the implementation of MapReduce in cloud computing becomes a new trend due to its parallel computing characteristic. Since large ECG dataset consume much time in analysis processes, this project will construct a cloud computing approach for ECG analysis using MapReduce in order to investigate the effect of MapReduce in enhancing ECG analysis efficiency in cloud computing. The project is expected to reduce ECG analysis process time for large ECG dataset

MinHop (MH) Transmission strategy to optimized performance of epidemic routing protocol

Delay tolerant network aims to provide the network architecture in environments where end-to-end path may never exist for long duration of time Furthermore dynamic topology changes limited buffer space and non stable connectivity make routing a challenging issue The research contribution regarding DTN routing protocols can be categorized in to single and multi copy strategies A single copy strategy makes less use of network resources but suffers from long delay and less delivery probability Multi copy schemes enjoy better delivery probability and minimum delivery delay at the cost of heavy use of network resource Moreover DTN nodes operate under short contact duration and limited transmission bandwidth Therefore it is not possible for a node to transmit all messages from its forwarding queue Hence the order at which the messages are forwarded becomes very vital In this paper we propose a forwarding queue mode named MinHop We prove through simulations that the proposed policy performs better then FIFO in terms of delivery probability overhead message drop and rela

Threshold Based Best Custodian Routing Protocol for Delay Tolerant Network

Delay Tolerant Network (DTN) is a kind of network in which the source may not be able to establish the stable and uninterrupted path to destination due to network partitioning, dynamic topology change and frequent disconnections. In order to dealt disruption and disconnections a store, carry and forward paradigm is used in which node stores the incoming messages in its buffer, carries it while moving and forward when comes within the transmission range of other nodes. Message forwarding contributes and important role in increasing its delivery. For instance, probabilistic routing protocol forwards message to a node having high probability value to meet message destination. These protocols cannot handle a situation in which the node continually transmits messages even the probability difference is very small. In this paper, we have proposed a routing protocol known as Threshold Based best custodian Routing Protocol (TBbcRP) for delay tolerant network. We have proposed a threshold-based method to compute the quality value which is the ability of node to carry message. A self-learning mechanism has been used to remove the delivered messages from the network. Moreover, a buffer aware mechanism has been used that make sure availability of buffer space at receiver before message transmission. We have compared the performance of TBbcRP with Epidemic, PRoPHET and Delegated Forwarding. The proposed TBbcRP outperforms in terms of maximizing the delivery probability, reducing number of transmissions and message drop

Clinical pathway variance prediction using artificial neural network for acute decompensated heart failure clinical pathway

Patients in modern healthcare demand superior healthcare quality. Clinical pathways are introduced as the main tools to manage this quality. A clinical pathway is a task-oriented care plan that specifies steps to be taken for patient care. It follows the clinical course according to the specific clinical problem. During clinical pathway execution, variance or deviation from the specified care plan could occur, and may endanger the patient’s life. In this paper, a proposed framework for artificial neural networks (ANNs) in clinical pathway variance predictions is presented. This proposed research method predicts the variance that may occur during Acute Decompensated Heart Failure Clinical Pathway. By using the Artificial Neural Network, 3 variances (Dialysis, PCI, and Cardiac Catherization) are predicted from 55 input. The results show that artificial neural networks with the Levenberg-Marquadt training algorithm with a 55-27-27-1 architecture achieve the best prediction rate, with an average prediction accuracy of 87.4425% for the training dataset and 85.255% for the test dataset

Route Path Selection Optimization Scheme Based Link Quality Estimation and Critical Switch Awareness for Software Defined Networks

Software-defined network (SDN) is a new paradigm that decouples the control plane and data plane. This offered a more flexible way to efficiently manage the network. However, the increasing number of traffics due to the proliferation of the Internet of Things (IoT) devices also increase the number of flow arrival which in turn causes flow rules to change more often, and similarly, path setup requests increased. These events required route path computation activities to take place immediately to cope with the new network changes. Searching for an optimal route might be costly in terms of the time required to calculate a new path and update the corresponding switches. However, the current path selection schemes considered only single routing metrics either link or switch operation. Incorporating link quality and switch’s role during path selection decisions have not been considered. This paper proposed Route Path Selection Optimization (RPSO) with multi-constraint. RPSO introduced joint parameters based on link and switches such as Link Latency (LL), Link Delivery Ratio (LDR), and Critical Switch Frequency Score (CWFscore). These metrics encourage path selection with better link quality and a minimal number of critical switches. The experimental results show that the proposed scheme reduced path stretch by 37%, path setup latency by 73% thereby improving throughput by 55.73%, and packet delivery ratio by 12.5% compared to the baseline work

Adaptive message size routing strategy for delay tolerant network

Delay tolerant network (DTN) is a kind of computer network that suffer from the frequent disconnections, network partitioned and unstable network connectivity, therefore maintaining an uninterrupted route from source to destination is not possible. Therefore, the transmission of message is achieved via intermediate nodes by adopting a novel transmission mechanism called store-carry and forward where node stores the incoming message in its buffer, carries it while moving and forward it when it comes in the transmission range of other nodes. DTN routing protocols can be either single copy or multi copy. In single copy protocols, the node forwards the unique copy of message along a single path. These protocols suffer the long delivery delay. In multi copy protocols, the node diffuses multiple copies of same message along dissimilar paths. Thus, message can reach destination via more than one path. However, the replication process consumes high volume of network resources such as buffer space, bandwidth and node energy. The probabilistic routing strategies for instance PRoPHET Protocol minimizes the consumption of resources and forwards a message to a custodian by using a metric of delivery probability. The probability describes the suitability of a node to meet the destination of message. However, when node mobility pattern is not symmetric the probabilistic computations cannot predict the accurate forwarding decision. In this paper, we have proposed a novel message forwarding strategy called Adaptive Message-Size Routing strategy (AMRS) by which a node handovers the copy of message to its neighboring nodes by using a metric named as mean threshold (MTH). We have compared the performance of AMRS with Epidemic and PRoPHET routing protocols. The proposed routing strategy has performed better in terms of maximizing delivery probability while minimizes message drops and number of transmissions

Auto-tuned hadoop mapreduce for ECG analysis

Electrocardiograph (ECG) analysis brings a lot of technical concerns because ECG is one of the tools frequently used in the diagnosis of cardiovascular disease. According to World Health Organization (WHO) statistic in 2012, cardiovascular disease constitutes about 48% of noncommunicable deaths worldwide. Although there are many ECG related researches, there is not much efforts in big data computing for ECG analysis which involves dataset more than one gigabyte. ECG files contain graphical data and the size grows as period of data recording gets longer. Big data computing for ECG analysis is critical when many patients are involved. Recently, the implementation of Hadoop MapReduce in cloud computing becomes a new trend due to its parallel computing characteristic which is preferable in big data computing. Since large ECG dataset consume much time in analysis processes, this project will construct a cloud computing approach for ECG analysis using MapReduce in order to investigate the effect of MapReduce in enhancing ECG analysis efficiency in cloud computing. However, the performance of existing MapReduce approach is limited to its configuration based on many factors such as behaviors of cluster and nature of computing processes. Hence, this research proposes MapReduce Auto-Tuning approach using Genetic Algorithm (GA) to enhance MapReduce performance in cloud computing for ECG analysis. The project is expected to reduce ECG analysis process time for large ECG dataset compared to default Hadoop MapReduce

A Review of Interpretable ML in Healthcare: Taxonomy, Applications, Challenges, and Future Directions

We have witnessed the impact of ML in disease diagnosis, image recognition and classification, and many more related fields. Healthcare is a sensitive field related to people’s lives in which decisions need to be carefully taken based on solid evidence. However, most ML models are complex, i.e., black-box, meaning they do not provide insights into how the problems are solved or why such decisions are proposed. This lack of interpretability is the main reason why some ML models are not widely used yet in real environments such as healthcare. Therefore, it would be beneficial if ML models could provide explanations allowing physicians to make data-driven decisions that lead to higher quality service. Recently, several efforts have been made in proposing interpretable machine learning models to become more convenient and applicable in real environments. This paper aims to provide a comprehensive survey and symmetry phenomena of IML models and their applications in healthcare. The fundamental characteristics, theoretical underpinnings needed to develop IML, and taxonomy for IML are presented. Several examples of how they are applied in healthcare are investigated to encourage and facilitate the use of IML models in healthcare. Furthermore, current limitations, challenges, and future directions that might impact applying ML in healthcare are addressed