Web-Based System for Advanced Heart Disease Identification Using Grid Computing Technology

Han, Chang Hee; Youn, Chan‐Hyun; Jung, Wooram

doi:10.1109/cbms.2008.106

Cited by 6 publications

(1 citation statement)

References 4 publications

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“…In year 2008 Han C. H. et.al [4] presents that strategy MCG (Magnetocardiogram) is additionally utilized for coronary illness recognition like the ECG (Electrocardiogram). The author presumes that it can't give the propelled e-Health administrations in light of immense measure of information, which may be handled and overseen.…”

Section: Literature Surveymentioning

confidence: 99%

Evaluating Predictive Algorithms using Receiver-Operative Characteristics for Coronary Illness among Diabetic Patients

Mahboob¹,

Sahaheen²,

Tahir³

et al. 2017

ijacsa

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Abstract-The grouping of information is a typical method in Machine learning. Information mining assumes a crucial part to extract learning from vast databases from operational databases. In medicinal services Data mining is a creating field of high significance, giving expectations and a more profound comprehension of restorative information sets. Most extreme information mining technique relies on an arrangement of elements that characterizes the conduct of the learning calculation furthermore straightforwardly or by implication impact of the multifaceted nature of models. Coronary illness is the main sources of death over the past years. Numerous scientists utilize a few information digging methods for the diagnosing of coronary illness. Diabetes is one of the incessant maladies that emerge when the pancreas does not deliver enough insulin. The vast majority of the frameworks have effectively utilized Machine learning strategies, for example, Naïve Bayes Algorithm, Decision Trees, logistic Regression and Support Vector Machines to name a few. These techniques solely rely on grouping of the information with respect to finding the heart variations from the norm. Bolster vector machine is an advanced strategy has been effectively in the field of machine learning. Utilizing coronary illness determination, the framework presented predicts using characteristics such as, age, sex, cholesterol, circulatory strain, glucose and the odds of a diabetic patient getting a coronary illness using machine learning algorithms.

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Section: Literature Surveymentioning

confidence: 99%

Evaluating Predictive Algorithms using Receiver-Operative Characteristics for Coronary Illness among Diabetic Patients

Mahboob¹,

Sahaheen²,

Tahir³

et al. 2017

ijacsa

View full text Add to dashboard Cite

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An overview of online based platforms for sharing and analyzing electrophysiology data from big data perspective

Chen

Wang

Yuan

2017

WIREs Data Min & Knowl

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With the development of applications and high‐throughput sensor technologies in medical fields, scientists and scientific professionals are facing a big challenge—how to manage and analyze the big electrophysiological datasets created by these sensor technologies. The challenge exhibits several aspects: one is the size of the data (which is usually more than terabytes); the second is the format used to store the data (the data created are generally stored using different formats); the third is that most of these unstructured, semi‐structured, or structured datasets are still distributed over many researchers' own local computers in their laboratories, which are not open access, to become isolated data islands. Thus, how to overcome the challenge and share/mine the scientific data has become an important research topic. The aim of this paper is to systematically review recent published research on the developed web‐based electrophysiological data platforms from the perspective of cloud computing and programming frameworks. Based on this review, we suggest that a conceptual scientific workflow‐based programming framework associated with an elastic cloud computing environment running big data tools (such as Hadoop and Spark) is a good choice for facilitating effective data mining and collaboration among scientists. WIREs Data Mining Knowl Discov 2017, 7:e1206. doi: 10.1002/widm.1206 This article is categorized under: Application Areas > Health Care Fundamental Concepts of Data and Knowledge > Information Repositories Technologies > Computer Architectures for Data Mining

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Policy-based hybrid workflow management system for heart disease identification

Jung¹,

Youn²,

Kim³

et al. 2009

2009 15th Asia-Pacific Conference on Communications

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Web-Based System for Advanced Heart Disease Identification Using Grid Computing Technology

Cited by 6 publications

References 4 publications

Evaluating Predictive Algorithms using Receiver-Operative Characteristics for Coronary Illness among Diabetic Patients

Evaluating Predictive Algorithms using Receiver-Operative Characteristics for Coronary Illness among Diabetic Patients

An overview of online based platforms for sharing and analyzing electrophysiology data from big data perspective

Policy-based hybrid workflow management system for heart disease identification

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