Study of Machine Learning Algorithms for Prediction and Diagnosis of Cardiovascular Diseases : A Review
DOI:
https://doi.org//10.32628/CSEIT2062136Keywords:
Cardiovascular disease, Heart, Machine learning, Prediction, Training, Data Mining.Abstract
We are living in a post modern era and there are tremendous changes happening to our daily life which make an impact on our health positively and negatively. As a result of these changes various kind of diseases are enormously increased. In the medical field, the diagnosis of cardiovascular disease is the most difficult task. The diagnosis of cardiovascular disease is difficult as a decision relied on grouping of large clinical and pathological data. Due to this complication, the interest increased in a significant amount between the researchers and clinical professionals about the efficient and accurate heart disease prediction. In case of heart disease, the correct diagnosis in early stage is important as time is the very important factor. Heart disease is the principal source of deaths widespread, and the prediction of Heart Disease is significant at an untimely phase. Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has provided the greatest support for predicting disease with correct case of training and testing. This research paper intends to provide a survey of techniques of knowledge discovery in databases using data mining techniques that are in use in today’s medical research particularly in Cardiovascular Disease Prediction.
References
- M. Fatima, M. Pasha” Survey of Machine Learning Algorithms for Disease Diagnostic” Journal of Intelligent Learning Systems and Applications, 2017, 9, 1-16
- V.V. Ramalingam, Ayantan Dandapath, M Karthik Raja “Heart disease prediction using machine learning techniques: A Survey” International Journal of Engineering & Technology, 7 (2.8), 2018, 684-687
- Mr. Chala Beyene, Pooja Kamat ”Survey on Prediction and Analysis the Occurrence of Heart Disease Using Data Mining Techniques” International Journal of Pure and Applied Mathematics , ijpam Volume 118 No. 8 ,2018, 165-174
- N. Satish Chandra Reddy, Song Shue Nee, Lim Zhi Min & Chew Xin Ying” Classification and Feature Selection Approaches by Machine Learning Techniques: Heart Disease Prediction.”International Journal of Innovative Computing , IJIC Vol. 9:1, 2019,39-46
- P. Suresh and M.D. Ananda Raj ”Study and Analysis of Prediction Model for Heart Disease: An Optimization Approach using Genetic Algorithm” International Journal of Pure and Applied Mathematics , ijpam, Volume 119, No. 16, 2018, 5323-5336
- 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, 2017.
- A. Davari Dolatabadi, S. E. Z. Khadem, and B. M. Asl, “Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM,” Comput. Methods Programs Biomed., vol. 138, 2017,pp. 117–126.
- C. Colak, E. Karaman, and M. G. Turtay, “Application of knowledge discovery process on the prediction of stroke,” Comput. Methods Programs Biomed., vol. 119, no. 3,2015, pp. 181–185.
- M. Gandhi, “Predictions in Heart Disease Using Techniques of Data Mining,” Int. Conf. Futur. Trend Comput. Anal. Knowl. Manag., 2015.
- S. Kiruthika Devi, S. Krishnapriya, and D. Kalita, “Prediction of heart disease using data mining techniques,” Indian J. Sci. Technol., vol. 9, no. 39,2016 pp. 21–24.
- U. R. Acharya et al., “Application of higher-order spectra for the characterization of Coronary artery disease using electrocardiogram signals,” Biomed. Signal Process. Control, vol. 31,2017, pp. 31–43
- E. K. Hashi, M. S. U. Zaman, and M. R. Hasan, “An expert clinical decision support system to predict disease using classification techniques,” 2017 Int. Conf. Electr. Comput. Commun. Eng.,2017 pp. 396–400.
- M. Shahi and R. Kaur Gurm, “Heart disease prediction system using data mining techniques,” Orient. J Comput. Sci. Technol., vol. 6, no. 4, 2013,pp. 457–466.
- Priti Chandra; M. A. Jabbar ; B. L. Deekshatulu “Computational intelligence technique for early diagnosis of heart disease,” IEEE International Conference on Engineering and Technology (ICETECH), 10.1109/ICETECH.2015.7275001.
- S. M. S. Shah, S. Batool, I. Khan, M. U. Ashraf, S. H. Abbas, and S. A. Hussain, “Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis,” Phys. A Stat. Mech. Its Appl., vol. 482, 2017,pp. 796–807.
- M. Saqlain, W. Hussain, N. A. Saqib, and M. A. Khan, “Identification of Heart Failure by Using Unstructured Data of Cardiac Patients,” 2016 45th Int. Conf. Parallel Process. Work.,2016, pp. 426–431.
- Centers for Disease Control and Prevention (CDC), "Heart disease fact sheet” https://www.cdc.gov/dhdsp/data_statistics/fact_sheets/fs_heart_disease.htm, 2015.
- E. J. Benjamin, S. S. Virani, C. W. Callaway, A. R. Chang, S. Cheng, S. E. Chiuve, M. Cushman, and F. N. Delling, et. al., "Heart disease and stroke statistics-2018 update: a report from the American Heart Association," Circulation, Vol. 137, No. 12, pp. 67- 492, 2018.
- T. Revathi, S. Jeevitha “ Comparative Study on Heart Disease Prediction System Using Data Mining Techniques” International Journal of Science and Research (IJSR) ISSN (Online) Volume 4 Issue 7, July 2015.
- R. Rao, “Survey on Prediction of Heart Morbidity Using Data Mining Techniques,” Knowl. Manag., vol.1, no. 3, pp. 14–34, 2011.
- M. C. Staff, “Heart disease,” Mayo Clinic. Online]. Available: http://www.mayoclinic.org/diseasesconditions/ heart-disease/symptoms-causes/dxc-20341558
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