Abstract

Data mining is becoming gradually popular and vital to healthcare organizations, finding useful patterns in complex data, transforming it into beneficial information for decision making. The latest statistics of WHO and UNICEF show that annually approximately 55,000 women die due to preventable pregnancy-related causes in India. Therefore, the current focus of health care researchers is to promote the use of e-health technology in developing countries. There have been many studies that apply data mining methods to recognize solutions for health care limitations in obstetrics and maternal care domain. Some of those studies included high risk pregnancy, prediction of preeclampsia, Identification of obstetric risk factors, discovering the risk factors of preterm birth, and predicting risk pregnancy in women performing voluntary interruption of pregnancy. This paper provides a survey and analysis of data mining methods that have been applied to maternal care domain.

References

• M. Jamal Afridi and Muddassar Farooq, OG-Miner: an Intelligent Health Tool For Achieving Millennium Development Goals (MDGs) in m-Health Environments, IEEE, Proceedings of the 44th Hawaii International Conference on System Sciences, pages 1-10, 2011
• Sónia Pereira, Filipe Portela, Manuel Filipe Santos, José Machado, António Abelha, Predicting Type of Delivery by Identification of Obstetric Risk Factors through Data Mining, Elsevier, Procedia Computer Science Volume 64 , 601 – 609, 2015
• Hsiang-Yang Chen, Chao-Hua Chuang , Yao-Jung Yang, Tung-Pi Wu , Exploring the risk factors of preterm birth using data mining, Elsevier, Expert Systems with Applications, Volume 38, Issue 5, May 2011, Pages 5384–5387, 2011
• L.M. Taft ,R.S. Evans , C.R.Shyua, M.J., Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery, Elsevier, Journal of Biomedical Informatics, Volume 42, Issue 2, April 2009, Pages 356–364, 2009
• U. Fund, “State of the World’s Children 2009. Maternal and Newborn Health,” New York: UNICEF, vol. 60, 2008.
• http://www.who.int/gho/maternal_health/en/
• http://unicef.in/Whatwedo/1/Maternal-Health
• Problem and pitfalls of risk assessment in antenatal care, An International journal of Obstetrics and Gynecology
• Tom M. Mitchell Machine learning algorithms enable discovery of important “regularities” in large data sets, communications of the ACM November 1999/vol. 42, no. 11
• Clark, P. and Niblett, R. The CN2 induction algorithm. Mach. Learn. 3, 4 (Mar. 1989), 261–284.
• Yu Chen, Lars Henning Pedersen, Wesley W. Chu,, Jorn Olsen: Drug Exposure Side Effects from Mining Pregnancy Data, ACM, Volume 9 Issue 1, Pages22–29,June-2007.
• Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2nd ed. ISBN 1-55860-901-6,March,2006.
• L. Kenny, W. Dunn, D. Ellis, J. Myers, P. Baker, and D. Kell, “Novel biomarkers for preeclampsia detected using metabolomics and machine learning,” Metabolomics, vol. 1, no. 3, pp. 227–234, 2005.
• Hsiang-Yang Chen, Ting-Wei Houa, Chao-Hua Chuang, TBPS Research Group, Applying data mining to explore the risk factors of parenting stress, Elsevier, Expert Systems with Applications 37 598–601,2010
• Dr. Meenu Dave, Priyanka Dadhich, International Journal of Information, Communication and Computing Technology, Vol 1, JUL 2013
• Hetal Bhavsar, Amit Ganatra, A Comparative Study of Training Algorithms for Supervised Machine Learning, International Journal of Soft Computing and Engineering, Volume-2, Issue-4, September 2012.
• Ajayi Adebowale, Idowu S.A, Anyaehie Amarachi A ,Comparative Study of Selected Data Mining Algorithms Used For Intrusion Detection, International Journal of Soft Computing and Engineering, Volume-3, Issue-3, July 2013
• A. S. Galathiya , A. P. Ganatra , and C. K. Bhensdadia , Improved Decision Tree Induction Algorithm with Feature Selection, Cross Validation, Model Complexity and Reduced Error Pruning, IJCSIT, Vol. 3 (2) ,3427-3431,2012.