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A Comparative Review of Recent Data Mining Techniques for Prediction of Cardiovascular Disease from Electronic Health Records

  • M. Sivakami
  • P. PrabhuEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 38)

Abstract

Cardiovascular disease is one of the key diseases spreading all over the world. Conventional method of curing ailment generates voluminous patient data which are left uncared during and after treatment. The collected data can be mined systematically using various tools & techniques and the wealth of information obtained could systematically assist the clinicians’ in decision making. Literature has cited a plenty of work concerning the stated problems using various data mining techniques are reviewed here critically. The reported accuracy level clearly revealed that the arrival of distinct decision needs more accuracy for better decision making support for doctors. Hence the mining of electronic health records using hybrid model which combines benefits of various algorithms for pre-processing and modeling may provide more insight to discussed problem with improved scalability, speed and accuracy.

Keywords

Data mining Heart disease Prediction Decision making Modeling Hybrid techniques 

Notes

Acknowledgements

This research work was carried out with the financially support of RUSA-Phase 2.0 grant sanctioned vide Letter No. F24-51/2014-U, Policy (TNMulti-Gen) Dept. of Edn. Govt of India, Dt.09.10.2018 at Alagappa University, Karaikudi, Tamilnadu, India.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ApplicationsAlagappa UniversityKaraikudiIndia
  2. 2.Directorate of Distance Education, Department of Computer ApplicationsAlagappa UniversityKaraikudiIndia

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