Open Access
Issue
ITM Web Conf.
Volume 40, 2021
International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
Article Number 03039
Number of page(s) 5
Section Computing
DOI https://doi.org/10.1051/itmconf/20214003039
Published online 09 August 2021
  1. Series, R.D., Health Risk Factors in Rapidly Changing Economies. [Google Scholar]
  2. Ratner, R.E., Christophi, C.A., Metzger, B.E., Dabelea, D., Bennett, P.H., Pi-Sunyer, X., Fowler, S., Kahn, S.E. and Diabetes Prevention Program Research Group, 2008. Prevention of diabetes in women with a history of gestational diabetes: effects of metformin and lifestyle interventions. The Journal of Clinical Endocrinology & Metabolism, 93 (12),pp.4774–4779. [Google Scholar]
  3. Goossens, J., Beeckman, D., Van Hecke, A., Delbaere, I. and Verhaeghe, S., 2018. Preconception lifestyle changes in women with planned pregnancies. Midwifery, 56, pp.112–120. [Google Scholar]
  4. Anand, A. and Shakti, D., 2015, September. Prediction of diabetes based on personal lifestyle indicators. In 2015 1st International Conference on Next Generation Computing Technologies (NGCT) (pp. 673–676). IEEE. [Google Scholar]
  5. Patil, M., Lobo, V.B., Puranik, P., Pawaskar, A., Pai, A. and Mishra, R., 2018, July. A proposed model for lifestyle disease prediction using support vector machine. In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1–6). IEEE. [Google Scholar]
  6. Sharma, M. and Majumdar, P.K., 2009. Occupational lifestyle diseases: An emerging issue. Indian journal of occupational and environmental medicine, 13(3), p.109. [Google Scholar]
  7. Sharma, R., Biedenharn, K.R., Fedor, J.M. and Agarwal, A., 2013. Lifestyle factors and reproductive health: taking control of your fertility. Reproductive biology and endocrinology, 11(1),pp.1–15. [Google Scholar]
  8. Coutinho, E.D.C., Silva, C.B.D., Chaves, C.M.B., Nelas, P.A.B., Parreira, V.B.C., Amaral, M.O. and Duarte, J.C., 2014. Pregnancy and childbirth: What changes in the lifestyle of women who become mothers?. Revista da Escola de Enfermagem da USP, 48 (SPE2), pp.17–24. [Google Scholar]
  9. Hemsing, N., Greaves, L. and Poole, N., 2017. Preconception health care interventions: a scoping review. Sexual & reproductive healthcare, 14, pp.24–32 [Google Scholar]
  10. Yi-Wei Chen and Chih-Jen Lin. Combining SVMs with Various Feature Selection Strategies. Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 207), 2006 [Google Scholar]
  11. Desbordes Paul RuanSuModzelewski Romain V auclin Sebastien V era Pierre Gardin Isabelle. Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier. Computerized Medical Imaging and Graphics, Dec 2016 [Google Scholar]
  12. Hanchuan Peng, Fuhui Long, and Chris Ding. Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Transaction on pattern analysis and machine intelligence Vol. 27, No. 8, August 2005 [Google Scholar]
  13. Chien-Pang Lee, Yungho Leu. A novel hybrid feature selection method for microarray data analysis. Elsevier Journal Applied Soft Computing 11 (2011) 208–213 [Google Scholar]
  14. Hui-Huang Hsu, Cheng-Wei Hsieh, Ming-Da Lu. Hybrid feature selection by combining filters and wrappers. Elsevier Journal Expert Systems with Applications 38 (2011) 8144–8150 [Google Scholar]
  15. P. Jaganathan, N. Rajkumar, and R. Kuppuchamy. A Comparative Study of Improved F-Score with Support Vector Machine and RBF Network for Breast Cancer Classification. International Journal of Machine Learning and Computing, Vol. 2, No. 6, December 2012 [Google Scholar]
  16. Huijuan Lu, Junying Chen, Ke Yan, QunJin, Yu Xue, Zhigang Gao, A Hybrid Feature Selection Algorithm for Gene Expression Data Classification, Neurocomputing (2017) [Google Scholar]
  17. Harun Uguz, A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm, Knowledge-Based Systems. Volume 24, Issue 7,October 2011, Pages1024–1032 [Google Scholar]
  18. Huilin Zheng, Hyun Woo Park, Dingkun Li, Kwang Ho Park, Keun Ho Ryu. A Hybrid Feature Selection Approach for Applying to Patients with Diabetes Mellitus: KNHANES 2013-2015. 2018 5th NAFOSTED Conference on Information and Computer Science (NICS) [Google Scholar]
  19. Himani Deshpande, Leena Ragha, April 22, 2021, “Mother’s Significant Feature (MSF) Dataset”, IEEE Dataport, doi: https://dx.doi.org/10.21227/kq5k-b784. [Google Scholar]
  20. Fang, L., Jiang, H. and Cui, S., 2017, July. An improved decision tree algorithm based on mutual information. In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) (pp. 1615–1620). IEEE. [Google Scholar]
  21. Deshpande H., Ragha L., (in press). A Hybrid Random Forest based Feature selection model using Mutual Information and F- score for Preterm birth classification., International Journal of Medical Engineering and Informatics. [Google Scholar]

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