:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies
http://TuEngr.com
ISSN 2228-9860
eISSN 1906-9642
CODEN: ITJEA8
FEATURE PEER-REVIEWED ARTICLE
Vol.11(15) (2020) |
-
Efficient Diagnostic Cardiac System using Machine Learning Approach
Mujtaba Ashraf Qureshi
(Department of Information Technology, Mewar University, Chittorgarh (Raj), INDIA),
Azad Kumar Shrivastava (Department of Computer Science, Mewar University, Chittorgarh (Raj), INDIA).
Disciplinary: Computer and Information Technology, Cardiology (Cardiovascular Health and Disease).
DOI: 10.14456/ITJEMAST.2020.294
Keywords: Data mining; neural networks; feature selection; WEKA tool; Cardiovascular disease prediction; Cardiac disease prediction.
AbstractHeart disease is considered one of the ultimate threats to human life. To predict cardiac diseases in the early stages has become a challenge to medical science. Machine learning has acted as a rescuer to assist and develop various cardiac diagnostic systems. Data mining techniques mostly used as a synonym to machine learning plays an important role to mine useful knowledge. However, machine learning (ML) emphasis more on the prediction of diverse diseases. In this research work, three models are devised to predict cardiovascular diseases using artificial neural networks. Models are devised based on the application of a different number of hidden layers. The backpropagation algorithm is used to calculate the desired value by the adjustment of weights of the neurons in the network. In the very last stage of the experimental work performance measures of the three devised models are compared to reach the most efficient model.Paper ID: 11A15F
Cite this article:
Qureshi, M.A., Shrivastava, A.K. (2020). Efficient Diagnostic Cardiac System using Machine Learning Approach.International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 11(15), 11A15F, 1-8.
References
- C.S. Dangare, S. Apte. A Data Mining Approach for Prediction of Heart Disease Using Neural Networks. International Journal of Computer Engineering and Technology, 2012, 3(3).
- A.Dewan, M. Sharma. Prediction of Heart Disease Using a Hybrid Technique in Data Mining Classification. 2nd International Conference on Computing for Sustainable Global Development, 2015, 704-706.
- S.A.Pattekari and A.Parveen. Prediction system for heart disease using naive Bayes. International Journal of Advanced Computer and Mathematical Science, 2012, 3(3), 290-294.
- B.S. Rao, K.N. Rao, S.P. SETTY. An Approach for Heart Disease Detection by Enhancing Training Phase of Neural NetworkUsing Hybrid Algorithm. Advance Computing Conference, 2014, 1211-1220.
- Vanisree K, J. Singaraju. Decision Support System for Congenital Heart Disease Diagnosis based on Signs and Symptoms using Neural Networks. International Journal of Computer Application, 2011, 19(6).
- R. Das, I. Turkoglu, A. Sengur. Effective Diagnosis of Heart Disease through Neural Network Ensemble. Expert Systems with Applications, 2009, 36(4), 7675-7680. DOI: 10.1016/j.eswa.2008.09.013.
- P.Gupta, B. Kaur. Accuracy Enhancement of Artificial Neural Network using Genetic Algorithm. International Journal of Computer Applications, 2014, 103(13).
- Rani, K. U. Analysis of heart disease dataset using a neural network approach. 2011.
- Tu, J.V. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of clinical epidemiology, 1996, 49(11), 1225-1231.
- Ashraf, Mudasir, Majid Zaman, and Muheet Ahmed. "To Ameliorate Classification Accuracy Using Ensemble Vote Approach and Base Classifiers." Emerging Technologies in Data Mining and Information Security, Springer, Singapore, 2019. 321-334.
- Ashraf, Mudasir, Majid Zaman, and Muheet Ahmed. Performance analysis and different subject combinations: An empirical and analytical discourse of educational data mining. 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2018.
- Nang Y. The Handbook of data mining. Lawrence Erlbaum associates, 2003.
Other issues:
Vol.11(12)(2020)
Vol.11(11)(2020)
Vol.11(10)(2020)
Vol.11(9)(2020)
Vol.11(8)(2020)
Vol.11(7)(2020)
Vol.11(6)(2020)
Vol.11(5)(2020)
Vol.11(4)(2020)
Vol.11(3)(2020)
Vol.11(2)(2020)
Vol.11(1)(2020)
Archives
Call-for-Papers
Call-for-Scientific Papers
Call-for-Research Papers: ITJEMAST invites you to submit high quality papers for full peer-review and possible publication in areas pertaining engineering, science, management and technology, especially interdisciplinary/cross-disciplinary/multidisciplinary subjects.
To publish your work in the next available issue, your manuscripts together with copyright transfer document signed by all authors can be submitted via email to Editor @ TuEngr.com (no space between). (please see all detail from Instructions for Authors)
Publication and peer-reviewed process:
After the peer-review process (4-10 weeks), articles will be on-line published in the available next issue. However, the International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies cannot guarantee the exact publication time as the process may take longer time, subject to peer-review approval and adjustment of the submitted articles.