Analyzing uncertainty in cardiotocogram data for the prediction of fetal risks based on machine learning techniques using rough set

Author:

Kannan E.,Ravikumar S.,Anitha A.,Kumar Sathish A. P.,Vijayasarathy M.

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Reference48 articles.

1. Afridi R, Iqbal Z, Khan M, Ahmad A, Naseem R (2019) Fetal heart rate classification and comparative analysis using cardiotocography data and known classifiers. Int J Grid Distrib Comput 12(1):31–42

2. Amin B, Gamal M, Salama AA, Mahfouz K, El-Henawy IM (2019) Classifying cardiotocography data based on rough neural network. Mach Learn 10:8

3. Anitha A, Kannan E (2014a) Isolating critical data points from boundary region with feature selection. In: 2014 IEEE international conference on computational intelligence and computing research, (pp 1–4), IEEE

4. Anitha A, Kannan E (2014b) A constructive distance-based boundary detection approach with numeric variables. J Theor Appl Inf Technol 67:3

5. Ayres-de-Campos D, Bernardes J, Garrido A, Marques-de-Sa J, Pereira-Leite L (2000) SisPorto 2.0: a program for automated analysis of cardiotocograms. J Maternal-Fetal Med 9(5):311–318

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