Big Data and Neural Network in Cardiology Health Prediction
Agustín Joison, N.1*, Raul Barcudi, J.2, Enrique Majul, A.3, Sergio Ruffino, A.4, Juan DE Mateo Rey, J.5, Agustin Joison, M.6 & Gustavo Baiardi7
1Chairman of Biological Department, Faculty of Chemical Sciences, Catholic University of Córdoba, Avenida Armada Argentina
2Professor and Chairman of Coronary Unit, University Reina Fabiola Clinic, Córdoba, Argentina
3Chairman of Química III, Dean of Faculty of Health Sciences, Catholic University of Córdoba, Córdoba, Argentina
4Professor Assistant of Química III, Faculty of Health Sciences, Catholic University of Córdoba, Córdoba, Argentina
5Professor of Química III, Faculty of Health Sciences, Catholic University of Córdoba, Córdoba, Argentina
6Systems Engineer, Globant Business, Avenida Colón 610, Córdoba, Argentina
7Assistant professor, Institute of Biological and Technological Research (IIBYT-CONICET), National University of Córdoba, Faculty of Chemical Sciences, Catholic University of Córdoba, Avenida Armada Argentina 3555, Córdoba, Argentina
Dr. Agustín Joison, N., Chairman of Biological Department, Faculty of Chemical Sciences, Catholic University of Córdoba, Avenida Armada Argentina.
Keywords: Big Data; Cardiology; Algorithms; Neural Network; Health
In health as in other areas there is availability of information, which can be collected in digital and continuous format; this phenomenon that is currently called Big Data that includes four research themes in the current literature, namely: 1.information, 2. technology, 3. methods, 4. Impact. The term Big Data from 1990 began to be used as a synonym for the collection, automation and recording of large amounts of data, which introduced an innovation in the treatment of information improving decision-making in many areas of our society. In the area of cardiology health research, clinical diagnosis, prognosis and therapy of patients, accurate and orderly data are fundamental tools in the access and analysis of the right information. The current era of computer, systems and software enabled a better interpretation of Big Data, and improved deep learning and a successful advancement in machine learning algorithms. Big Data is now transformed into a tool that enables for healthcare stakeholders to implement three types of analysis techniques; 1: historical (descriptive study), 2: future results (predictive study), 3: current situation (prescriptive study). This technology, which was born with some uncertainty and Luke warmness of professionals to accept this revolution regarding the information it provides, ends as a new paradigm in health care by understanding of algorithms and machine learning importance.
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