Preterm Birth Prediction Using Cuckoo Search-Based Fuzzy Min-Max Neural Network
(*) Corresponding author
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
In the latest history, a Decision making and prediction system has been investigated vigorously for several decades and has got a lift. Together with preterm birth study, the decision support system has been explored in different areas. Using fuzzy, neural network and cuckoo search algorithm, the medical decision support system is improved for the forecast of preterm birth in this document. A two-module pattern categorization and rule extraction system has been highlighted by this study, where in the former module emphasises an altered fuzzy min–max (FMM) neural-network-based pattern classifier, whereas the subsequent module emphasises oppositional cuckoo search based rule extractor. With the theory of opposition, this paper examines altered cuckoo search algorithm. Using Pre Term Birth (PTB) datasets, the empirical analysis is executed and applied using MATLAB. Performance assessment matrix occupied is the precision and our suggested method is compared with the active methods. It is examined that our suggested method has attained improved precision value (85.6 %) when compared to FMM (77.36 %) which illustrates the efficiency of the suggested method from the results
Copyright © Praise Worthy Prize - All rights reserved.
Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth, "From Data Mining to Knowledge Discovery in Databases", AI Magazine, Vol. 17, pp. 37-54, 1996.
K. Yacoben and L. Carmichael, "Applying the Knowledge Discovery in Databases (KDD) Process to Fermilab Accelerator Machine Data", Fermi National Accelerator Laboratory, 1997.
Frawley, W., Piatetsky-Shapiro, G., and Matheus, C. “Knowledge Discovery in Databases: An Overview”. AI Magazine, pp: 57-70, 1992.
Osmar R. Zaiane, "Chapter I: Introduction to Data Mining", CMPUT690 Principles of Knowledge Discovery in Databases, 1999.
Thuraisingham, B.: “A Primer for Understanding and Applying Data Mining”, IT Professional, pp: 28-31, 2000.
J. P. Bigus, “Data Mining with Neural Networks”, McGraw-Hill, 1996.
Fayyad U., Piatetsky-Shapiro G., Smyth P., From data mining to knowledge discovery: an overview, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, Menlo Park, CA, AAAI/MIT Press, 1996, pp: 1-36.
Romero C., Ventura S., Espejo P.G., and Hervas C., Data Mining Algorithms to Classify Students, proceedings of the 1st Int'l conference on educational data mining, Canada, 2008, pp: 8-17.
Zhang J., Mani I., kNN Approach to Unbalanced Data Distributions: A Case Study involving Information Extraction, In Proceedings of The Twentieth International Conference on Machine Learning (ICML-2003), Workshop on Learning from Imbalanced Data Sets II, August 21, 2003.
J. R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufman Publishers, 1993.
W. W. Cohen, “Fast effective rule induction,” in Proc. of the 12th Intl. Conf. on Machine Learning, 1995, pp. 115–123.
P. Langley, W. Iba, and K. Thompson, “An analysis of bayesian classifiers,” in National Conf. on Artigicial Intelligence, 1992, pp. 223-228.
V. Vapnik, The Nature of Statistical Learning Theory. Springer Verlag, 1995.
R. Andrews, J. Diederich, and A. Tickle, “A survey and critique of techniques for extracting rules from trained artificial neural networks,” Knowledge Based Systems, vol. 8, no. 6, pp. 373–389, 1995.
T. G. Dietterich, “Ensemble methods in machine learning,” Lecture Notes in Computer Science, vol. 1857, pp. 1–15, 2000.
Catley.C, Frize. M, Walker C.R. and Petriu D.C., “Predicting High-Risk Preterm Birth Using Artificial Neural Networks”, IEEE Transactions on Information Technology in Biomedicine, vol.10, no.3, pp.540-549, 2006.
Catley.C, Frize. M, Walker C.R. and Petriu D.C., “Predicting preterm birth using artificial neural networks”, 18th IEEE Symposium on Computer-Based Medical Systems, pp.103-108, 2005.
Diab M.O., Marque C and Khalil M.A., “Unsupervised Classification in Uterine Electromyography Signal: Toward The Detection of Preterm Birth”, 27th Annual International Conference of the Engineering in Medicine and Biology Society, pp.5660-5663, 2006.
AnasQuteishat, CheePeng Lim, and Kay Sin Tan, "A Modified Fuzzy Min–Max Neural Network With a Genetic-Algorithm-Based Rule Extractor for Pattern Classification", IEEE Transactions On Systems, Man, And Cybernetics—Part A: Systems And Humans, Vol. 40, No. 3, May 2010.
Kenneth Lim, MD, Vancouver BC, Kimberly Butt, MD, Fredericton NB, Joan M. Crane, MD, and St. John’s NL, "Ultrasonographic Cervical Length Assessment in Predicting Preterm Birth in Singleton Pregnancies", SOGC Clinical Practice Guideline, no.257, 2011.
Datasets from http://stat.duke.edu/courses/Fall10/sta216/ddedata2.txt
Ben Ayed, D., Arous, N., Ellouze, N., Phoneme classification using different approaches based on fuzzy measurements and neural networks, (2009) International Review on Computers and Software (IRECOS), 4 (2), pp. 205-211.
Dhifallah, J.B.S., Laabidi, K., Lahmari, M.K., Classification methods based on pattern recognition and on neural networks for failure detection, (2010) International Review on Computers and Software (IRECOS), 5 (3), pp. 257-263.
Zakrani, A., Idri, A., Applying radial basis function neural networks based on fuzzy clustering to estimate web applications effort, (2010) International Review on Computers and Software (IRECOS), 5 (5), pp. 516-524.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2022 Praise Worthy Prize