Overview and Comparison of Machine Learning Methods to Build Classification Model for Prediction of Categorical Outcome Based on Medical Data View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018

AUTHORS

Andrea Peterkova , German Michalconok , Allan Bohm

ABSTRACT

In this paper a classification model is proposed to predict a future state of patient’s cardiac diagnosis based on a large amount of medical data. The methodology of building a prediction model can be applied also to the other areas, such as industrial processes. In our research, we focus on cardiologic datasets of selected patients who were indicated for the ischemic heart disease. The selected sample of patients is divided into four stages of clinical diagnosis. Some of the parameters have a significant impact on the probability of the occurrence of the myocardial infraction. For building a classification model to predict categorical class output was used STATISTICA 13 software. More... »

PAGES

216-224

References to SciGraph publications

  • 2005. Support Vector Machines in DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK
  • 1998-01. Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2003-01. A Taxonomy of Dirty Data in DATA MINING AND KNOWLEDGE DISCOVERY
  • Book

    TITLE

    Cybernetics Approaches in Intelligent Systems

    ISBN

    978-3-319-67617-3
    978-3-319-67618-0

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-67618-0_20

    DOI

    http://dx.doi.org/10.1007/978-3-319-67618-0_20

    DIMENSIONS

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