Online citations, reference lists, and bibliographies.
← Back to Search

An Intelligent Real-time Heart Diseases Diagnosis Algorithm

Rooh Ullah Khan, Tassadaq Hussain, Hanan Quddus, Amna Haider, Adnan Adnan, Zahid Mehmood
Published 2019 · Computer Science

Save to my Library
Download PDF
Analyze on Scholarcy Visualize in Litmaps
Reduce the time it takes to create your bibliography by a factor of 10 by using the world’s favourite reference manager
Time to take this seriously.
Get Citationsy
Among all diseases, the heart diseases are responsible for highest number of deaths in the world especially in undeveloped countries. One of the reasons for this high mortality rate is due to delayed diagnosis based on ECG records. On the other hand, the heart diseases are computationally complex and intensive, and are difficult to manually analyze. Therefore, with the increase in the growth of heart disease databases and information, a high performance computing and easy to use programming environment is required to solve medical application. In this work we have proposed development an Intelligent Heart Diseases Diagnosis Algorithm (IHDDA) which reads heart signals (e.g. ECG graphs etc.) and reaches on a flawless diagnosis in fraction of seconds. The proposed algorithm executed on a supercomputing cluster and tested with 300 patients of different heart problems, the results shows that the IHDDA identify heart problem in 1.5 seconds with an accuracy of 97%.
This paper references
G. G. Stokes (1890)
Grant's clinical electrocardiography : the spatial vector approach
R. P. Grant (1970)
Principles of Clinical Electrocardiography
M. J. Goldman (1973)
Coronary angiogram interpretation. Interobserver variability.
J. E. Galbraith (1978)
and N
J. E. Galbraith (1978)
Understanding the electrocardiogram: A new approach
D. J. Rowlands (1981)
Neural network design
M. Hagan (1995)
Prospective validation of artificial neural network trained to identify acute myocardial infarction
W. Baxt (1996)
Acute myocardial infarction detected in the 12-lead ECG by artificial neural networks.
B. Hedén (1997)
and L
B. Hedén (1997)
Identification of patients at increased risk of first unheralded acute myocardial infarction by electron-beam computed tomography.
P. Raggi (2000)
Clinical applications of artificial neural networks: Theory
R. Dybowski (2001)
ACC/AHA guideline update for perioperative cardiovascular evaluation for noncardiac surgery---executive summary a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to Update the 1996 Guidelines on Perioperative Cardiovascular Evaluat
K. Eagle (2002)
ROUSHDY. ”A Medical Multimedia Expert System for Heart Diseases Diagnosis and Training.
N. RAGAB (2004)
Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success
Kensaku Kawamoto (2005)
Genetic Algorithms and Machine Learning
D. Goldberg (2005)
Diagnostic accuracy of noninvasive coronary angiography using 64-slice spiral computed tomography.
G. Raff (2005)
and D
K. Kawamoto (2005)
Common errors in computer electrocardiogram interpretation.
M. Guglin (2006)
New imaging techniques for diagnosing coronary artery disease
E. Escolar (2006)
and N
E. Escolar (2006)
Errors in the computerized electrocardiogram interpretation of cardiac rhythm.
A. Shah (2007)
Electrograms (ECG, EEG, EMG, EOG).
R. Reilly (2010)
Diagnosis Of Heart Disease Using Datamining Algorithm
A. Rajkumar (2010)
Heart disease and stroke statistics 2016 update
A. S. Go (2010)
Implementation of a Reverse Time Migration kernel using the HCE High Level Synthesis tool
Tassadaq Hussain (2011)
Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction
Soni Jyoti (2011)
and E
T. Hussain (2011)
I and J
W. Marsden (2012)
and E
T. Hussain (2012)
and T
M. A. Hanson (2013)
A novel access pattern-based multi-core memory architecture
Tassadaq Hussain (2014)
and E
S. Chetlur (2014)
and S
T. Hussain (2014)
and M
T. Hussain (2014)
T. Hussain (2014)
Prediction of Disease Level Using Multilayer Perceptron of Artificial Neural Network for Patient Monitoring
M. O. G. Nayeem (2015)
The clinical use of stress echocardiography in non-ischaemic heart disease: recommendations from the European Association of Cardiovascular Imaging and the American Society of Echocardiography.
P. Lancellotti (2016)
D. Mozaffarian (2016)
P. Lancellotti (2017)
EMVS: Embedded Multi Vector-core System
Tassadaq Hussain (2018)
and E
T. Hussain (2018)
Coronavirus Will Be in the Top 10 Causes of Death
" Intelligent Heart Disease Prediction System Using Data Mining Techniques "
Ms. Ishtake

This paper is referenced by
Semantic Scholar Logo Some data provided by SemanticScholar