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How to Increase the Effectiveness of the Hepatitis Diagnostics by Means of Appropriate Machine Learning Methods

  • Alexandra Lukáčová
  • František Babič
  • Zuzana Paraličová
  • Ján ParaličEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9267)

Abstract

This paper presents how to improve the diagnostic process of hepatitis B and C based on collected questionnaires from patients hospitalized in all regional departments of infectology in Slovakia. Performed experiments were oriented in two directions: economic demands of the recommended treatment based on realized diagnostics and possible improvement of hepatitis diagnostics by means of exploratory and predictive analysis of additional information provided by patients. Exploratory data analysis was used to confirm or to reject some expected relationships between input attributes (e.g. ager or gender) and target diagnosis. Also, predictive mining resulted into interesting decision rules that can be used in medical practice as supporting information at an early stage of the diagnostic process. Finally, analysis of the treatment economic demands based on the estimated costs showed the need for timely and quality diagnostics to minimize the percentage of patients for which was hepatitis diagnosed late.

Keywords

Hepatitis CHAID Cost-benefit method 

Notes

Acknowledgment

This publication is the result of the Project implementation: University Science Park TECHNICOM for Innovation Applications Supported by Knowledge Technology, ITMS: 26220220182, supported by the Research & Development Operational Programme funded by the ERDF (50 %); supported also by the Slovak Grant Agency of the Ministry of Education and Academy of Science of the Slovak Republic under grant No. 1/1147/12 (50 %).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alexandra Lukáčová
    • 1
  • František Babič
    • 1
  • Zuzana Paraličová
    • 2
  • Ján Paralič
    • 1
    Email author
  1. 1.Faculty of Electrical Engineering and Informatics, Department of Cybernetics and Artificial IntelligenceTechnical University of KošiceKošiceSlovakia
  2. 2.Medical FacultyUniversity of Pavol Jozef ŠafárikKošiceSlovakia

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