A New Cygnus Optimization Algorithm for Prediction Of Cardio Vascular Disease

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.