A Review on Hypertensive Disorder in High Risk Pregnency

Authors

  • Shriya Acharya  Research Scholar, S.V.I.T, Vasad, Gujarat, India
  • Sneha Gaywala  Research Scholar, S.V.I.T, Vasad, Gujarat, India
  • Paresh Patel  Research Scholar, S.V.I.T, Vasad, Gujarat, India

Keywords:

Hypertension, Pregnancy

Abstract

Data mining plays important role in prediction of diesease in health care industry .many algorithms are developed for prediction of various diesease in this review paper we research for the hypertensive disorder during pregnency and research the different data mining techniiques used for hypertensive disorder in high risk pregnancy.

References

  1. Mario.W.L. Moreira., P.C.Rodrigues,Antonio M. B. Oliveira, "An Inference Mechanism Using Bayes-based Classifiers in Pregnancy care",IEEE Publication2016.
  2. Mario W. L., Moreira, Joel J. , P. C. Rodrigues Antonio M. B., Oliveira., "A Preeclampsia Diagnosis Approach using Bayesian Networks", IEEE Publication, 2015.
  3. Wenshuai Cheng., Liying Fang, Lin Yang., Han Zhao.,Pu Wang. , Jianzhuo Yan.,"Varying Coefficient Models for Analyzing the Effects of Risk Factors on Pregnant Women's Blood Pressure", IEEE Publication, 2014.
  4. Ranjan Das., Pallavi Vajinepalli., Rajendra Sisodia., and Lalit Gupta,"Automatic Doppler Signal Analysis to Assess Utero-Placental Circulation for Identifying High Risk Pregnancies, IEEE" publication,2013
  5. Rutvij Mehta., Nikita Bhatt, Amit Ganatra.,"A Survey on Data Mining Technologies for Decision Support System of Maternal Care".International Journal of Computer Applications.
  6. R. Das., P. Vajinepalli., R. Sisodia., and L. Gupta., "Automatic Doppler signal analysis to assess utero-placental circulation for identifying high risk pregnancies," in 2013 35th Annual International Conference of theIEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan,
  7. W. Cheng., L. Fang., L. Yang., H. Zhao, P. Wang., and J. Yan., "Varying coefficient models for analyzing the effects of risk factors on pregnant women’s blood pressure," in Machine Learning and Applications(ICMLA), 2014 13th International Conference on, 2014
  8. S. Saha., S. Biswas., and S. Acharyya., "Gene selection by sample classification using k nearest neighbor and meta-heuristic algorithms," in Advanced Computing (IACC), 2016 IEEE 6th International Conferenceon, Bhimavaram, India, Feb 27-28, 2016
  9. Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques", Elsevier.
  10. R. Mukherjeea., C. D. Ral., C. Chakrabortya., S. Dasgupta., and K.Chaudhury., "Clinical biomarker for predicting preeclampsia in women with abnormal lipid profile: statistical pattern classification approach," in International Conference on Systems in Medicine and Biology, Kharagpur, 2010.
  11. Mario W. L. Moreira., Joel J. P. C. Rodrigues Antonio M. B. Oliveira2., Kashif Saleem,and Augusto J. Ven.,ancio Neto.," Predicting Hypertensive Disorders in High-risk Pregnancy Using the Random Forest Approach". IEEE ICC 2017 SAC Symposium E-Health Track.
  12. www.wikipedia.com

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2018-04-30

Issue

Volume 4, Issue 4, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shriya Acharya, Sneha Gaywala, Paresh Patel, " A Review on Hypertensive Disorder in High Risk Pregnency, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 4, pp.1470-1474, March-April-2018.