Analysis and Prediction of Heart Health using Deep Learning Approach
DOI:
https://doi.org/10.26438/ijcse/v7i8.309315Keywords:
Machine learning, Medical Data Mining, Heart Disease, Tensor Flow, Deep Neural NetworkAbstract
Medical data mining is a tremendously significant domain for exploration because of its importance in the expansion of innumerable applications in the medical domain. On the fact of briefing the deaths taking place globally, the heart disease seems as the foremost cause of death. The recognition of the chance of heart disease in an individual is a complex task for health specialists because it requires years of experience and intense medical tests to be conducted. In this research work, enhanced deep neural network (DNN) learning is introduced to treat patients accurately and for maintaining consistency in heart disease prediction system. So that anticipation of the loss of lives at the prior stage is possible. The results formulated ideally verify that the designed diagnostic scheme is able of calculating the risk level of heart disease efficiently when compared to other methodologies. The proposed model provides better results in heart diseases prediction compared to that of previous work. Early prediction of the disease reduces the costs and time of the treatment. The cost and time of treatment will be reduced due to the early prediction of heart disease.
References
[1] Mai Shouman, Tim Turner and Rob Stocker, “Using data mining techniques in heart disease diagnosis and treatment”, Japan-Egypt Conference on Electronics, Communications and Computers (JEC- ECC), 2012.
[2] Pagidipati, N. J., & Gaziano, T. A., “Estimating Deaths From Cardiovascular Disease: A Review of Global Methodologies of Mortality Measurement. Circulation”, 127(6), 749–756, 2013. doi:10.1161/circulationaha.112.128413.
[3] K. Subhadra, Vikas B, "Neural Network Based Intelligent System for Predicting Heart Disease", International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-5,pp. 484-487, March 2019
[4] Zhi-Gen Hu, Jian-Ping Li, “Research And Application Of Data Warehouse And Data Mining Technology In Medical Field”, 12th International Computer Conference On Wavelet Active Media Technology And Information Processing (ICCWAMTIP), IEEE 2015.
[5] Kamran Farooq et al., “Clinical Decision Support Systems: A Visual Survey”.
[6] J. C. Prather et al., “Medical data mining: knowledge discovery in a clinical data warehouse”, Proc AMIA Annu Fall Symp. 1997: 101–105.
[7] M. Thiyagaraj, G. Suseendran, “Survey on heart disease prediction system based on data mining techniques”, Indian Journal of Innovations and Developments Vol 6(1), January, 2017.
[8] Jaymin Patel et al., “Heart Disease Prediction Using Machine learning and Data Mining Technique”, volume 7, pp. 129-137, March 2016.
[9] Rairikar, A., Kulkarni, V., Sabale, V., Kale, H., & Lamgunde, A. , “ Heart disease prediction using data mining techniques”, 2017 International Conference on Intelligent Computing and Control (I2C2).
[10] Dr. B. Umadevi, “A Survey on Prediction of Heart Disease Using Data Mining Techniques”, International Journal of Science and Research (IJSR), 2015.
[11] Ambekar, S., & Phalnikar, R., “Disease Risk Prediction by Using Convolutional Neural Network”, Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 2018. doi:10.1109/iccubea.2018.8697423.
[12] Gavhane, A., Kokkula, G., Pandya, I., & Devadkar, P. K., “ Prediction of Heart Disease Using Machine Learning. 2018 Second International Conference on Electronics”, Communication and Aerospace Technology (ICECA), 2018.doi:10.1109/iceca.2018.8474922.
[13] Jabbar, M. A., & Samreen, S., “Heart disease prediction system based on hidden naïve bayes classifier”, 2016 International Conference on Circuits, Controls, Communications and Computing (I4C), 2016.doi:10.1109/cimca.2016.8053261.
[14] Ritika, Chadha, Mayank, Shubhankar, “Prediction of heart disease using data mining techniques”, Springer 2016. DOI: https://doi.org/10.1007/s40012-016-0121-0.
[15] Shaikh, S., Sawant, A., Paradkar, S., & Patil, K., “Electronic recording system-heart disease prediction system”, 2015 International Conference on Technologies for Sustainable Development (ICTSD), 2015.doi:10.1109/ictsd.2015.7095854.
[16] R. Kannan, V. Vasanthi, “Machine Learning Algorithms with ROC Curve for Predicting and Diagnosing the Heart Disease”, Springer Briefs in Forensic and Medical Bioinformatics, pp 63-72, 14 June 2018. DOI: https://doi.org/10.1007/978-981-13-0059-2_8.
[17] Sowmiya, C., & Sumitra, P., “Analytical study of heart disease diagnosis using classification techniques”, 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), 2017.doi:10.1109/itcosp.2017.8303115.
[18] M. Sultana, A. Haider, and M. S. Uddin, "Analysis of data mining techniques for heart disease prediction", 2016 3rd Int. Conf. Electr. Eng. Inf. Commun. Technol. iCEEiCT 2016.
[19] Vikas Chaurasia, Saurabh Pal, “Early Prediction of Heart Diseases Using Data Mining Techniques”, Caribbean Journal of Science and Technology, Vol. 1, pp. 208-21, December 2013.
[20] Yogita Solanki, Sanjiv Sharma, "A Survey on Risk Assessments of Heart Attack Using Data Mining Approaches", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.11, No.4, pp. 43-51, 2019. DOI: 10.5815/ijieeb.2019.04.05.
[21] Niyati I. Patel , Hiren R. Patel, “A Survey on Prediction of Disease with Data Mining”, International Journal of Computer Sciences and Engineering(IJCSE), Survey Paper Vol.-7, Issue-2, E-ISSN: 2347-2693,pp. 289-293, Feb 2019.
[22] H. Benjamin Fredrick David and S. Antony Belcy, “Heart Disease Prediction Using Data Mining Techniques”, Department of Computer Science and Engineering, Manonmaniam Sundaranar University, India, ICTACT JOURNAL ON SOFT COMPUTING, OCTOBER 2018.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
