A Cloud Based Four-Tier Architecture for Early Detection of Heart Disease with Machine Learning Algorithms

Ahmed, Md. Razu; Mahmud, Sakib; Hossin, Md Altab; Jahan, Hosney; Noori, Sheak Rashed Haider

doi:10.1109/compcomm.2018.8781022

Cited by 29 publications

(8 citation statements)

References 15 publications

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“…Tier 1 can be used to collect data, and tier 2 can be used to store huge volumes of data. Tier 3 and tier 4 can be used for machine learning classifications and representation of the results, respectively [ 14 ]. In the present time, as computing power is not an issue, DNN has more than 20 layers.…”

Section: Discussionmentioning

confidence: 99%

“…There are 3 layers in an ANN, and every node in each layer is connected with all the nodes in the other layers. By increasing the number of hidden layers, a deeper neural network can be created [ 14 ]. In neural networks, there are three types of functions.…”

Section: Machine Learning Approaches and Algorithmsmentioning

confidence: 99%

“…Peculiar glucose metabolism, extreme blood pressure, dyslipidaemia, smoking, lack of physical exercise, and age are few risk factors that have been identified for major heart diseases. Symptoms of a heart disease may include shortness of breath, weakness of physical body, swollen feet and fatigue with related signs, and so on [ 14 ].…”

Section: Machine Learning In Disease Prediction and Detectionmentioning

confidence: 99%

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Involvement of Machine Learning Tools in Healthcare Decision Making

Jayatilake

Ganegoda

2021

Journal of Healthcare Engineering

125

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In the present day, there are many diseases which need to be identified at their early stages to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason, there is a need of analysing complex medical data, medical reports, and medical images at a lesser time but with greater accuracy. There are even some instances where certain abnormalities cannot be directly recognized by humans. In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden relationships or abnormalities which are not visible to humans. Implementing algorithms to perform such tasks itself is difficult, but what makes it even more challenging is to increase the accuracy of the algorithm while decreasing the required time for the algorithm to execute. In the early days, processing of large amount of medical data was an important task which resulted in machine learning being adapted in the biological domain. Since this happened, the biology and biomedical fields have been reaching higher levels by exploring more knowledge and identifying relationships which were never observed before. Reaching to its peak now the concern is being diverted towards treating patients not only based on the type of disease but also their genetics, which is known as precision medicine. Modifications in machine learning algorithms are being performed and tested daily to improve the performance of the algorithms in analysing and presenting more accurate information. In the healthcare field, starting from information extraction from medical documents until the prediction or diagnosis of a disease, machine learning has been involved. Medical imaging is a section that was greatly improved with the integration of machine learning algorithms to the field of computational biology. Nowadays, many disease diagnoses are being performed by medical image processing using machine learning algorithms. In addition, patient care, resource allocation, and research on treatments for various diseases are also being performed using machine learning-based computational decision making. Throughout this paper, various machine learning algorithms and approaches that are being used for decision making in the healthcare sector will be discussed along with the involvement of machine learning in healthcare applications in the current context. With the explored knowledge, it was evident that neural network-based deep learning methods have performed extremely well in the field of computational biology with the support of the high processing power of modern sophisticated computers and are being extensively applied because of their high predicting accuracy and reliability. When giving concern towards the big picture by combining the observations, it is noticeable that computational biology and biomedicine-based decision making in healthcare have now become dependent on machine learning algorithms, and thus they cannot be separated from the field of artificial intelligence.

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Section: Discussionmentioning

confidence: 99%

Section: Machine Learning Approaches and Algorithmsmentioning

confidence: 99%

Section: Machine Learning In Disease Prediction and Detectionmentioning

confidence: 99%

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Involvement of Machine Learning Tools in Healthcare Decision Making

Jayatilake

Ganegoda

2021

Journal of Healthcare Engineering

125

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“…A strong instruction for achieving fast and elastic healthcare application on cloud platforms was presented in this study. Recently, in [18], a cloud-based 4-tier architecture was proposed that consists of four components comprising of data collection unit, data storage unit, analysis unit, and application presentation unit. The general supervised learning machine methods such as support vector machine (SVM), artificial neural network (ANN), random forest (RF), Naïve Bayes (NB), and decision tree (DT) techniques were used for early prediction of heart failures.…”

Section: Remote Health Monitoring Framework and Architectures In Iotmentioning

confidence: 99%

A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

Akhbarifar

Javadi

Rahmani

et al. 2020

Pers Ubiquit Comput

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Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients’ health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients’ health condition continuously before any serious disorder or infection occur. According to transferring the huge volume of produced sensitive health data of patients who do not want their private medical information to be revealed, dealing with security issues of IoT data as a major concern and a challenging problem has remained yet. Encountering this challenge, in this paper, a remote health monitoring model that applies a lightweight block encryption method for provisioning security for health and medical data in cloud-based IoT environment is presented. In this model, the patients’ health statuses are determined via predicting critical situations through data mining methods for analyzing their biological data sensed by smart medical IoT devices in which a lightweight secure block encryption technique is used to ensure the patients’ sensitive data become protected. Lightweight block encryption methods have a crucial effective influence on this sort of systems due to the restricted resources in IoT platforms. Experimental outcomes show that K-star classification method achieves the best results among RF, MLP, SVM, and J48 classifiers, with accuracy of 95%, precision of 94.5%, recall of 93.5%, and f-score of 93.99%. Therefore, regarding the attained outcomes, the suggested model is successful in achieving an effective remote health monitoring model assisted by secure IoT data in cloud-based IoT platforms.

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“…Lastly, it can be used as an addition to the web client's GUI for the output and feedback. This available health, as well as environmental, data can be used as a source of huge data [31][32][33][34][35][36] for the statistical and epidemiological forecasting of upcoming epidemic diseases. This gateway's primary job is to support various wireless features and control deviceto-device communication.…”

mentioning

confidence: 99%

An Intelligent Health Care System in Fog Platform with Optimized Performance

Tripathy

Rath²,

Tripathy³

et al. 2023

Sustainability

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Cloud computing delivers services through the Internet and enables the deployment of a diversity of apps to provide services to many businesses. At present, the low scalability of these cloud frameworks is their primary obstacle. As a result, they are unable to satisfy the demands of centralized computer systems, which are based on the Internet of Things (IoT). Applications such as disease surveillance and tracking and monitoring systems, which are highly latency sensitive, demand the computation of the Big Data communicated to centralized databases and from databases to cloud data centers, resulting in system performance loss. Recent concepts, such as fog and edge computing, offer novel approaches to data processing by relocating the processing power and other resources closer to the end user, thereby reducing latency and maximizing energy efficiency. Existing fog models, on the other hand, have a number of limitations and tend to prioritize either the precision of their findings or a faster response time, but not both. For the purpose of applying a healthcare solution in the real world, we developed and implemented a one-of-a-kind architecture that integrates quartet deep learning with edge computing devices. The paradigm that has been developed delivers health management as a fog service through the Internet of Things (IoT) devices and efficiently organizes the data from patients based on the requirements of the user. FogBus, a fog-enabled cloud framework, is used to measure the effectiveness of the proposed structure in regards to resource usage, network throughput, congestion, precision, and runtime. To maximize the QoS or forecast the accuracy in different fog computing settings and for different user requirements, the suggested technique can be set up to run in a number of different modes.

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A Cloud Based Four-Tier Architecture for Early Detection of Heart Disease with Machine Learning Algorithms

Cited by 29 publications

References 15 publications

Involvement of Machine Learning Tools in Healthcare Decision Making

Involvement of Machine Learning Tools in Healthcare Decision Making

A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

An Intelligent Health Care System in Fog Platform with Optimized Performance

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