PREDICTION OF CARDIOVASCULAR DISEASE ON TRANSTHORACIC ECHOCARDIOGRAPHY DATA USING ARTIFICIAL NEURAL NETWORK

Artificial Neural Network Algorithm for Predicting the Heart Disease

Authors

  • Dr Chaithra N Division of Medical Statistics, School of Life Sciences, JSS academy of higher Education and Research
  • Raviraja S Medical Informatics, Sri Siddartha Academy of Higher Education
  • Sunil Kumar S Department of Cardiology, JSS Medical College, JSS Academy of Higher Education & Research
  • Ranjini V Department of Cardiology, JSS Medical College, JSS Academy of Higher Education & Research

Keywords:

Cardiovascular Disease, Transesophageal Echocardiography Data, Ischemic Heart disease, Artificial Neural Network

Abstract

 According to World Bank Epidemiological modelling, India has the second highest rate of Cardiovascular Disease (CVD) mortality worldwide, at 2.5 million new cases occurring annually. Heart disorder is a condition that affects heart function. One of the main problems with heart conditions in estimating a person's risk of having insufficient blood supply to the heart. According to the World Health Statistics 2012 report, one in every three individuals in the world has high blood pressure, a condition that accounts for almost half of all fatalities from heart disease and stroke. Echocardiography is an ultrasound procedure that uses a projector to display moving images of the heart and is used to diagnose and assess a series of disorders. Authors have considered to analyse and review several recent research works on CVD and experimental models. The proposed retrospective experiment contained a total of 7304 patients Transesophageal echocardiography (TTE) records with no missing values were chosen for the research in that 1113 patients were diagnosed with Ischemic Heart disease (IHD) and 6191 normal patients were classified as the subject. 70% of patients data were used to train the Neural Network and the other 30% of patients data used to test the model.  This research work estimates the efficiency of the Artificial Neural Network model to investigate the factors contributing significantly to enhancing the risk of IHD as well as accurately predict the overall risk using Machine learning software: WEKA 3.8.5. and SPSS modeler. The resulting model performance has a higher accuracy rate (97.0%) and this makes it a very vital techniques for cardiologists to screen patients at potential risk of developing the disease

Downloads

Download data is not yet available.

References

Ansarullah SI, Sharma KP, Wahid A, Kirmani MM. Heart Disease Prediction System using Data Mining Techniques: A study: International Research Journal of Engineering and Technology (IRJET). 2016; 3(8):1375-1381.

McAloon CJ et al. The changing face of cardiovascular disease 2000–2012: an analysis of the World Health Organization global health estimates data. International journal of cardiology. 2016; 1(64):256-64.

Roman WP, Martin HD, Sauli E. Cardiovascular diseases in Tanzania: The burden of modifiable and intermediate risk factors. Journal of Xiangya Medicine. 2019; 4(33):1-13.

Prabhakaran D, Jeemon P, Roy A. Cardiovascular diseases in India: current epidemiology and future directions. Circulation. 2016; 133(16):1605-20.

Singh P, Singh S, Pandi Jain GS. Effective heart disease prediction system using data mining techniques: International Journal of Nanomedicine. 2018; 13: 121– 124.

Nag T and Ghosh A. Cardiovascular disease risk factors in Asian Indian population: a systematic review. Journal of cardiovascular disease research. 2013; 4(4):222-8.

Prabhakaran D, Jeemon P, Sharma M, Roth GA, Johnson C, Harikrishnan S, et al. The changing patterns of cardiovascular diseases and their risk factors in the states of India: the global burden of disease study. 1990–2016. The Lancet Global Health. 2018; 6(12): e1352-63.

Kaddoura S. Echo made easy E-Book. 2nd edition. Toronto. Churchill Livingstone; 2016.

Malakar AK, Choudhury D, Halder B, Paul P, Uddin A, Chakraborty S. A review on coronary artery disease, its risk factors, and therapeutics. Journal of cellular physiology. 2019;234(10):16812-23.

Rheumatic Heart Disease. [online] Available at: <https://www.hopkinsmedicine.org/health/conditions-and-diseases/rheumatic-heart-disease> [Accessed 3 June 2020].

IOM (Institute of Medicine). 2010. Cardiovascular disability: updating the social security listings. Washington, DC: The National Academies Press.

Kaltman JR, Burns KM, Pearson GD, Goff DC, Evans F. Disparities in congenital heart disease mortality based on proximity to a specialized pediatric cardiac center. Circulation. 2020; 141(12):1-3.

Lohr JL and Sivanandam S. Introduction to echocardiography: in handbook of cardiac anatomy, physiology, and devices. Springer. 2015; p.85-397.

Ansarullah S I, Sharma P K, Wahid A, Kirmani M M. Heart disease prediction system using data mining techniques: a study. International Research Journal of Engineering and Technology. 2016; 3(8):1375- 1381.

Singh et al. Effective heart disease prediction system using data mining techniques. International Journal of Nanomedicine 2018;13: 121–124.

Rahmani A M et al. Machine Learning (ML) in Medicine: Review, Applications and Challenges. Mathematics. 2021, 9(22): 1-52.

Hannan SA, Manza RR, Ramteke RJ. Generalized Regression Neural Network and Radial Basis Function for Heart Disease Diagnosis: International Journal of Computer Applications.2010;7(13):7-13.

Karayılan T and Kilic O. Prediction of heart disease using neural network. International Conference on Computer Science and Engineering (UBMK). 2017: 719-723.

Gupta R et al. Artificial intelligence to deep learning: machine intelligence approach for drug discovery: Molecular diversity. 2021;25(3):1315-60.

Hamedi S, Kordrostami Z, Yadollahi A. Artificial neural network approaches for modeling absorption spectrum of nanowire solar cells: Neural Computing and Applications. 2019 ;31(12):8985-95.

Sayad AT and Halkarnikar PP. Diagnosis of heart disease using neural network approach 88 diagnosis of heart disease using neural network approach. International Journal of Advances in Science Engineering and Technology. 2014;2(3):88-92.

Oliveira et al. A System Based on Artificial Neural Networks for Automatic Classification of Hydro-generator Stator Windings Partial Discharges. Journal of Microwaves, Optoelectronics and Electromagnetic Applications. 2017; 16(3): 628-645.

Dwivedi AK. Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Computing & Application. 2018, 29 (10): 685–93.

Dangare Chaitrali S, Apte Sulabha S. (2012). A Data Mining Approach for Prediction of Heart Disease Using Neural Networks. International Journal of Computer Engineering and Technology (IJCET).2012; 3(3):30-40.

Published

24-04-2024

How to Cite

N, D. C., S, . R., S, S. K., & V, R. . (2024). PREDICTION OF CARDIOVASCULAR DISEASE ON TRANSTHORACIC ECHOCARDIOGRAPHY DATA USING ARTIFICIAL NEURAL NETWORK: Artificial Neural Network Algorithm for Predicting the Heart Disease. Communications in Mathematics and Applications, 15(1). Retrieved from http://www.rgnpublications.com/journals/index.php/cma/article/view/2590

Issue

Vol. 15 No. 1 (2024)

Section

Research Article

License

Creative Commons Licence 4.0 by  

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a CCAL that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.