Open Access Subscription Access
Open Access Subscription Access
An Efficient Heart Disease Detection System utilizing Naive Bayes Classification
Coronary illness is one of the most basic human infections on the planet and influences human life seriously. Heart-related maladies or Cardiovascular Diseases (CVDs) are the primary explanation behind countless passings on the planet in the course of the most recent couple of decades and has risen as the most perilous infection, in India as well as in the entire world. In coronary illness, the heart cannot push the necessary measure of blood to different pieces of the body. Exact and on-time analysis of coronary illness is significant for cardiovascular breakdown anticipation and treatment. The analysis of coronary illness through customary clinical history has been considered as not solid in numerous angles. Along these lines, there is a need for a solid, exact and practical framework to analyze such illnesses in an ideal opportunity for appropriate treatment. The proposed Naïve Bayes grouping framework can without much of a stretch distinguish and arrange individuals with coronary illness from solid individuals. The proposed Naïve Bayes order-based choice emotionally supportive network will help the specialists to conclusion heart patients productively. A significant test in Data Mining is to manufacture exact and computationally productive classifiers for clinical application. In this paper we considered order rule digging for information disclosure and produced the guidelines by applying our created approach on Heart expire databases [1, 2, 3].
Classification, Data Mining, Heart Disease, Naive Bayes.
- C. L. Blake, and C. J. Mertz, “UCI machine learning databases,” 2004. [Online]. Available: http://mlearn.ics.uci.edu/databases/heartdisease/
- G. R. Kumar, G. A. Ramachandra, and K. Nagamani, “An efficient feature selection system for integrating SVM with genetic algorithm for large medical datasets,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 4, no. 2, pp. 272-277, ISSN: 2277-128X, Feb. 2014.
- G. R. Kumar, V. S. Kongara, and G. A. Ramachandra, “An efficient ensemble based classification techniques for medical diagnosis,” International Journal of Latest Technology in Engineering, Management and Applied Sciences, vol. 2, no. 8, pp. 5-9, ISSN: 2278-2540, Aug. 2013.
- I. H. Witten, and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed., San Francisco: Morgan Kaufmann, 2005.
- H. G. Lee, K. Y. Noh, and K. H. Ryu, “Mining biosignal data: Coronary artery disease diagnosis using linear and nonlinear features of HRV,” LNAI 4819: Emerging Technologies in Knowledge Discovery and Data Mining, pp. 56-66, May 2007.
- J. Han, and M. Kamber, “Data mining concepts and techniques,” The Morgan Kaufmann series in Data Management Systems, 2nd ed., San Mateo, CA: Morgan Kaufmann, 2006.
- P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, A: Addision-Wesley, 2005.
- V. A. Sitar-Taut, et al., “Using machine learning algorithms in cardiovascular disease risk evaluation,” Journal of Applied Computer Science & Mathematics, vol. 3, no. 5, 2009.
- The Atlas of Heart Disease and Stroke. [Online]. Available: http://www.who.int/cardiovascular_diseases/resources/atlas/en/
- UCI Machine Learning Repository. [Online]. Available: https://archive.ics.uci.edu/ml/
Abstract Views: 8
PDF Views: 0