RETRACTED ARTICLE: Accuracy detection of coronary artery disease using machine learning algorithms

• DOI: 10.1007/s13204-021-02036-7
• Publication Date: 2021-08-27T18:02:36Z
• Authors: Harinder Singh; Tasneem Bano Rehman; Ch. Gangadhar; Rohit Anand; Nidhi Sindhwani; M. Vijaya Sekhar ...
• Keywords: Nanochemistry

Coronary artery disease, which involves a wide range of conditions, including narrowed or blocked coronary arteries, has remained the leading cause of death in the United States for over 50 years. The majority of cardiovascular disorders are preventable, which are identified through risk factors. Electrocardiogram (ECG), a routinely available test that provides information about one’s electrophysiologic health, may be beneficial in determining cardiovascular risk. Given the automated and highly correlated nature of its measurements, ECG data are suited well for analysis via machine learning. This research compares and demonstrates the improvements over standard methods for the discussed framework. The proposed framework demonstrates a novel approach to determine the severity of heart diseases using a traditional survival analysis and machine learning methods based on the blockages of major blood vessels in the heart. Hence, modern research demands to improve the accuracy of the predictive analysis. This work analyses the widespread predictive determination using various machine learning methods of heart disease and applies a cost-based matrix to enhance detection accuracy. An HD dataset to evaluate the classification performance following the classification algorithms.