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      A systematic review on machine learning approaches for cardiovascular disease prediction using medical big data

      , , , , ,
      Medical Engineering & Physics
      Elsevier BV

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          Support-vector networks

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            LIBSVM: A library for support vector machines

            LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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              Machine learning: Trends, perspectives, and prospects.

              Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Medical Engineering & Physics
                Medical Engineering & Physics
                Elsevier BV
                13504533
                July 2022
                July 2022
                : 105
                : 103825
                Article
                10.1016/j.medengphy.2022.103825
                35781385
                3f544b10-2f38-49f1-b7b8-c303a69c22c1
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

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