A New Cygnus Optimization Algorithm for Prediction Of Cardio Vascular Disease
K. Shyamala1, T. Marikani2
1K. Shyamala, Associate Professor*, P.G & Research Dept. of Computer Science, Dr. Ambedkar Government Arts College(Autonomous), Chennai, Tamil Nadu, India.
2T. Marikani, Assistant Professor, Dept. of Computer Science, Sree Muthukumaraswamy College, Affiliated to University of Madras, Chennai, Tamil Nadu, India
Manuscript received on September 18, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4351-4355 | Volume-8 Issue-12, October 2019. | Retrieval Number: L38681081219/2019©BEIESP | DOI: 10.35940/ijitee.L3868.1081219
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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: Machine learning is an emerging field in the present day due to a massive improvement in the size of data. However, with the current studies, the prologue of artificial intelligence and medical sciences, help in averting any such category of disease. With the intention to take good decision in health care, data mining methodology and technology plays a foremost role to transmit the enormous data into valuable information. Emerging as a primary source of fatality in the early 20th century and peaking in prevalence from 1980s, Cardio-Vascular Disease (CVD) remains a major global threat. Early prediction of CVD is most vital for sensible preclusion and treatment. This paper is to investigate the work on the attribute selection approach and propose an improved Cygnus Optimization Algorithm by carrying a random search through the whole attributes, which was obtained from UCI machine learning repository. The proposed method shows 97% of accuracy, which is better than early.
Keywords: Machine Learning, Data Mining Techniques, Naïve Bayes Classifier, Intelligent Data Analysis, Cygnus Optimization.
Scope of the Article: Machine learning