Data Mining Techniques to Find Out Heart Diseases: An Overview
Aqueel Ahmed1, Shaikh Abdul Hannan2
1Aqueel Ahmed, Department of Computer Science, Vasantrao Naik Mahavidyalaya, Aurangabad (M.S.), India.
2Shaikh Abdul Hannan, Department of Computer Science, Vivekanand College, Aurangabad (M.S.), India.
Manuscript received on October 01, 2012. | Revised Manuscript received on October 20, 2012. | Manuscript Published on September 10, 2012. | PP: 18-23 | Volume-1 Issue-4, September 2012. | Retrieval Number: D0253081412/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Heart disease is a major cause of morbidity and mortality in modern society. Medical diagnosis is extremely important but complicated task that should be performed accurately and efficiently. Although significant progress has been made in the diagnosis and treatment of heart disease, further investigation is still needed. The availability of huge amounts of medical data leads to the need for powerful data analysis tools to extract useful knowledge. There is a huge data available within the healthcare systems. However, there is a task of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous application in business and scientific domain. Researchers have long been concerned with applying statistical and data mining tools to improve data analysis on large data sets. Disease diagnosis is one of the applications where data mining tools are proving successful results. This research paper proposed to find out the heart diseases through data mining, Support Vector Machine (SVM), Genetic Algorithm, rough set theory, association rules and Neural Networks. In this study, we briefly examined that out of the above techniques Decision tree and SVM is most effective for the heart disease. So it is observed that, the data mining could help in the identification or the prediction of high or low risk heart diseases.
Keywords: Data Mining, Heart Disease, SVM, Rough Sets Techniques, Association rules & Clustering.