Analysis and Graphical Representation of Data Mining Techniques for Prediction of Heart Disease Using the Weka Tool
DOI:
https://doi.org/10.61841/hdydza63Keywords:
Data mining, Heart disease, WEKA, Naïve Base Classifier, K-nearest neighbor, Support vector machines and Random ForestAbstract
In current decades, heart disease has been recognized as like the leading cause of death throughout the world. However, it is considered so the most preventable or controllable disease at the same time. According in conformity with World Health Organization (WHO), the express and timely analysis of heart disease plays a remarkable role within preventing its progress and reducing related treatment costs. Data mining methods and machine learning algorithms play a very important function within this area. The researchers accelerating their research works to boost a software with the help machine learning algorithm which perform help doctors to take a decision regarding both prediction and diagnosing of heart disease. The main objective over this research paper is predicting the heart disease regarding a patient the use of machine learning algorithms. Comparative study concerning Naïve Base Classifier, K-nearest neighbor, Support vector machines and Random Forest the precision and recall about machine learning algorithms is performed through a graphical representation concerning the results.
Downloads
References
1. World Health Organization (2011) The top ten causes of death.
2. World Health Organization (2013) Deaths from coronary heart disease.
3. Center for Disease Control and Prevention (2014) Heart Disease and Family History.
4. Paladugu S (2010) Temporal mining framework for risk reduction and early detection of chronic diseases. University of Missouri-Columbia.
5. Obenshain MK (2004) Application of data mining techniques to healthcare data. Infection Control and Hospital Epidemiology 25: 690-695.
6. A, Roddick JF (2006) Towards role based hypothesis evaluation for health data mining. Electronic. Journal of Health Informatics 1: 1-9.
7. Porter T, Green B (2009) Identifying Diabetic Patients: A Data Mining Approach.
8. Panzarasa S, Quaglini S, Sacchi L, Cavallini A, Micieli G, et al. (2010) Data mining techniques for analyzing stroke care processes. In the Proc. of the 13th World Congress on Medical Informatics.
9. Li L, Tang H, Wu Z, Gong J, Gruidl M, et al. (2004) Data mining techniques for cancer detection using serum proteomic profiling. Artificial intelligence in medicine 32: 71-83.
10. Das R, Turkoglu I, Sengur A (2009) Effective diagnosis of heart disease through neural networks ensembles. Expert Systems with Applications 36: 7675-7680.
11. Lakshmi K, Krishna MV, Kumar SP (2013) Performance Comparison of Data Mining Techniques for Predicting of Heart Disease Survivability. International Journal of Scientific and Research Publications 3: 1-10.
12. [V.A. Medical Center, Long Beach Clinic Foundation, “Available: https://archive.ics.uci.edu/ml/datasets/Heart+Disease, [Last Accessed 5 November 2015].
13. A. S. Abdullah, R. R. Rajalaxmi, “A Data mining Model for predicting the Coronary Heart Diseaseusing Random Forest Classifier”, International Conference on Recent Trends in Computational Methods, Communication and Controls (ICON3C 2012), ICON3C(3), pp.22-25, April 2012.
14. K. Kalaiselvi, K. Sangeetha, S. Mogana, “Efficient Disease Classifier Using Data Mining Techniques: Refinement of Random Forest Termination Criteria”, IOSR Journal of Computer Engineering (IOSR-JCE), Vol. 14, No. 5, pp.104-111, 2013.
15. E. E. Tripoliti, D.I. Fotiadis, G. Manis, “Automated Diagnosis of Diseases Based on Classification: Dynamic Determination of the Number of Trees in Random Forests Algorithm” IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 16, NO. 4, JULY 2012.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
