EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 448 (2023) E3S Web Conf., 448 (2023) 02057 References
Open Access
Issue
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
Article Number 02057
Number of page(s) 10
Section Information System
DOI https://doi.org/10.1051/e3sconf/202344802057
Published online 17 November 2023
  1. L. C. Poon and K. H. Nicolaides, “Early Prediction of Preeclampsia,” Obstet. Gynecol. Int., vol. 2014, no. Table 2, pp. 1–11, 2014, doi: 10.1155/2014/297397. [Google Scholar]
  2. P. Von Dadelszen and L. A. Magee, “Pre-eclampsia: An Update,” Curr. Hypertens. Rep., vol. 16, no. 8, 2014, doi: 10.1007/s11906-014-0454-8. [CrossRef] [Google Scholar]
  3. H. Sufriyana, Y. W. Wu, and E. C. Y. Su, “Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia,” EBioMedicine, vol. 54, 2020, doi: 10.1016/j.ebiom.2020.102710. [CrossRef] [PubMed] [Google Scholar]
  4. J. Zhang et al., “Early prediction of preeclampsia and small-for-gestational-age via multi-marker model in Chinese pregnancies: A prospective screening study,” BMC Pregnancy Childbirth, vol. 19, no. 1, pp. 1–10, 2019, doi: 10.1186/s12884-019-2455-8. [CrossRef] [PubMed] [Google Scholar]
  5. L. Myatt, “Expert Review The prediction of preeclampsia : the way forward,” Am. J. Obstet. Gynecol., 2020, doi: 10.1016/j.ajog.2020.10.047. [Google Scholar]
  6. A. C. De Kat, J. Hirst, M. Woodward, S. Kennedy, and S. A. Peters, “Prediction models for preeclampsia: A systematic review,” Pregnancy Hypertens., vol. 16, no. March, pp. 48–66, 2019, doi: 10.1016/j.preghy.2019.03.005. [CrossRef] [Google Scholar]
  7. E. Purwanti and I. S. Preswari, “Early Risk Detection of Pre-eclampsia for Pregnant women using Artificial Neural Network,” vol. 15, no. 2, pp. 71–80, 2019. [Google Scholar]
  8. J. H. Jhee et al., “Prediction model development of late-onset preeclampsia using machine learning-based methods,” pp. 1–12, 2019. [Google Scholar]
  9. J. Allotey et al., “Development and validation of prediction models for risk of adverse outcomes in women with early-onset pre-eclampsia: protocol of the prospective cohort PREP study,” Diagnostic Progn. Res., vol. 1, no. 1, pp. 1–8, 2017, doi: 10.1186/s41512-016-0004-8. [CrossRef] [Google Scholar]
  10. M. A. Ganaie and M. Tanveer, “KNN weighted reduced universum twin SVM for class imbalance learning,” Knowledge-Based Syst., vol. 245, p. 108578, 2022, doi: 10.1016/j.knosys.2022.108578. [CrossRef] [Google Scholar]
  11. Z. Xu, D. Shen, T. Nie, Y. Kou, N. Yin, and X. Han, “A cluster-based oversampling algorithm combining SMOTE and k-means for imbalanced medical data,” Inf. Sci. (Ny)., vol. 572, pp. 574–589, 2021, doi: 10.1016/j.ins.2021.02.056. [CrossRef] [Google Scholar]
  12. M. M. Rahman and D. N. Davis, “Addressing the Class Imbalance Problem in Medical Datasets,” Int. J. Mach. Learn. Comput., no. May 2014, pp. 224–228, 2013, doi: 10.7763/ijmlc.2013.v3.307. [Google Scholar]
  13. S. Belarouci and M. A. Chikh, “Medical imbalanced data classification,” Adv. Sci. Technol. Eng. Syst., vol. 2, no. 3, pp. 116–124, 2017, doi: 10.25046/aj020316. [CrossRef] [Google Scholar]
  14. J. Kong, W. Kowalczyk, D. A. Nguyen, T. Back, and S. Menzel, “Hyperparameter Optimisation for Improving Classification under Class Imbalance,” 2019 IEEE Symp. Ser. Comput. Intell. SSCI 2019, pp. 3072–3078, 2019, doi: 10.1109/SSCI44817.2019.9002679. [Google Scholar]
  15. S. Singh and P. Gupta, “Comparative Study Id3, Cart and C4.5 Decision Tree Algorithm: a Survey,” Int. J. Adv. Inf. Sci. Technol. ISSN, vol. 27, no. 27, pp. 97–103, 2014. [Google Scholar]
  16. E. Budiman, Haviluddin, N. Dengan, A. H. Kridalaksana, M. Wati, and Purnawansyah, “Performance of Decision Tree C4.5 Algorithm in Student Academic Evaluation,” Lect. Notes Electr. Eng., vol. 488, no. February, pp. 380–389, 2018, doi: 10.1007/978-981-10-8276-4_36. [CrossRef] [Google Scholar]
  17. C. O. Truicǎ and C. A. Leordeanu, “Classification of an imbalanced data set using decision tree algorithms,” UPB Sci. Bull. Ser. C Electr. Eng. Comput. Sci., vol. 79, no. 4, pp. 69–84, 2017. [Google Scholar]
  18. D. A. Cieslak and N. V. Chawla, “Learning decision trees for unbalanced data,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5211 LNAI, no. PART 1, pp. 241–256, 2008, doi: 10.1007/978-3-540-87479-9_34. [Google Scholar]
  19. H. Ding, L. Chen, L. Dong, Z. Fu, and X. Cui, “Imbalanced data classification: A KNN and generative adversarial networks-based hybrid approach for intrusion detection,” Futur. Gener. Comput. Syst., vol. 131, pp. 240–254, 2022, doi: 10.1016/j.future.2022.01.026. [CrossRef] [Google Scholar]
  20. M. Beckmann, N. F. F. Ebecken, and B. S. L. Pires de Lima, “A KNN Undersampling Approach for Data Balancing,” J. Intell. Learn. Syst. Appl., vol. 07, no. 04, pp. 104–116, 2015, doi: 10.4236/jilsa.2015.74010. [Google Scholar]
  21. W. Xing and Y. Bei, “Medical Health Big Data Classification Based on KNN Classification Algorithm,” IEEE Access, vol. 8, pp. 28808–28819, 2020, doi: 10.1109/ACCESS.2019.2955754. [CrossRef] [Google Scholar]
  22. H. Dubey and V. Pudi, “Class based weighted K-Nearest neighbor over imbalance dataset,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7819 LNAI, no. PART 2, pp. 305–316, 2013, doi: 10.1007/978-3-642-37456-2_26. [Google Scholar]
  23. R. Guido, M. C. Groccia, and D. Conforti, “A hyper-parameter tuning approach for cost-sensitive support vector machine classifiers,” Soft Comput., vol. 27, no. 18, pp. 12863–12881, 2022, doi: 10.1007/s00500-022-06768-8. [Google Scholar]
  24. H. Jin, “Hyperparameter Importance for Machine Learning Algorithms,” pp. 1–8, 2022, [Online]. Available: http://arxiv.org/abs/2201.05132. [Google Scholar]
  25. B. Panda, “A survey on application of Population Based Algorithm on Hyperparameter Selection,” no. April, 2020, doi: 10.13140/RG.2.2.11820.21128. [Google Scholar]
  26. J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl, “Algorithms for hyper-parameter optimization,” Adv. Neural Inf. Process. Syst. 24 25th Annu. Conf. Neural Inf. Process. Syst. 2011, NIPS 2011, pp. 1–9, 2011. [Google Scholar]
  27. F. Zhang, M. Petersen, L. Johnson, J. Hall, and S. E. O’bryant, “Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer’s Disease Data,” Appl. Sci., vol. 12, no. 13, 2022, doi: 10.3390/app12136670. [Google Scholar]
  28. P. Kampstra, “V28C01-1,” vol. 28, no. November, pp. 1–9, 2008, [Online]. Available: papers3://publication/uuid/692988CE-7E10-498E-96EC-E7A0CE3620A3. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.