Cardiovascular Disease Analysis Using Python

Cardiovascular Disease Analysis Using Python

Nehas Reddy Kyatham

Electronics and Communication Engineering Institute of Aeronautical Engineering Hyderabad, India

N. Manish Kumar

Electronics and Communication Engineering Institute of Aeronautical Engineering Hyderabad, India

B. Sneha
Electronics and Communication Engineering Institute of Aeronautical Engineering Hyderabad, India

Abstract— Worldwide, cardiovascular diseases are still among the major health issues due to their prolonged healing process. In this paper, we present a novel machine learning technique for early CVD detection. Our system utilizes electro-cardiogram (ECG) data and focuses on feature selection optimization as a means of improving prediction accuracy. We achieved outstanding accuracy rates of 100 on both small and large datasets by using sophisticated classifiers. This approach has the potential to transform patient management practices and decrease CVD-related mortality rates. Cardiovascular disorders (CVDs) pose a major global health challenge accounting for high numbers of deaths across the globe(Siontis et al.,2011) (Lyon et al., 2011) .In this regard, we propose an innovative machine learning technique that is based on electrocardiograms (ECGs) to achieve remarkable accuracy in early CVD detection. Our system concentrates on feature selection optimization so as to enhance prediction scores. Using available state-of-the-art classifiers enabled us achieving 100% predictively  paling small databases including lessthan20,000 records as well as extensive ones containing millions of observations which is promising for transforming patients’ supervision systems.

Index Terms—CVD,CardioVascular,ECG