Detection of CAD using optimization approach with machine learning classification techniques
CAD Detection using AI
Keywords:Cardiovascular Diseases, Machine Learning, Optimization Techniques, Coronary Artery Diseases
One of most serious ailments among cardiovascular disorders is Coronary Artery Disease (CAD). One of the key concerns is the high expense of CAD detection conventional tools like angiography. In terms of high accuracy and cost-effective solutions, supervised learning models for the automatic classification of the CAD are an economical way. Using machine learning techniques to build a model for CAD detection provides the optimal approach so far. In health-care organizations, a large volume of data is generated. It assists researchers in making the most of large amounts of data in order to quickly and accurately diagnose the problem. The research's key objective is to use feature extraction and optimization approaches to create a machine learning model. The performance is assessed in this study employing four strong machine learning classification algorithms and two feature extraction algorithms, namely Independent Component Analysis (ICA) and Principal Component Analysis (PCA), as well as a hybridization of Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) for feature optimization.
Copyright StatementCopyright in a work is a bundle of rights. IJoSI's, copyright covers what may be done with the work in terms of making copies, making derivative works, abstracting parts of it for citation or quotation elsewhere and so on. IJoSI requires authors to sign over rights when their article is ready for publication so that the publisher from then on owns the work. Until that point, all rights belong to the creator(s) of the work. The format of IJoSI copy right form can be found at the IJoSI web site.
The issues of International Journal of Systematic Innovation (IJoSI) are published in electronic format and in print. Our website, journal papers, and manuscripts etc. are stored on one server. Readers can have free online access to our journal papers. Authors transfer copyright to the publisher as part of a journal publishing agreement, but have the right to:
1. Share their article for personal use, internal institutional use and scholarly sharing purposes, with a DOI link to the version of record on our server.
2. Retain patent, trademark and other intellectual property rights (including research data).
3. Proper attribution and credit for the published work.