Open Access
| Issue |
E3S Web Conf.
Volume 73, 2018
The 3rd International Conference on Energy, Environmental and Information System (ICENIS 2018)
|
|
|---|---|---|
| Article Number | 12012 | |
| Number of page(s) | 5 | |
| Section | Health, Safety and Environment Information Systems | |
| DOI | https://doi.org/10.1051/e3sconf/20187312012 | |
| Published online | 21 December 2018 | |
- A. Anguera, J. M. Barreiro, J. A. Lara, and D. Lizcano, Applying data mining techniques to medical time series: an empirical case study in electroencephalography and stabilometry, Comput. Struct. Biotechnol. J., vol. 14, pp. 185–199, 2016. [CrossRef] [PubMed] [Google Scholar]
- Hashima-E-Nasreen, M. Edhborg, M. Petzold, Y. Forsell, and Z. N. Kabir, Incidence and Risk Factor of Postpartum Depressive Symptoms in Women: A Population Based Prospective Cohort Study in a Rural District in Bangladesh, J. Depress. Anxiety, vol. 4, no. 2, pp. 4–11, 2015. [Google Scholar]
- C. El-Hachem et al., Early identification of women at risk of postpartum depression using the Edinburgh Postnatal Depression Scale (EPDS) in a sample of Lebanese women, BMC Psychiatry, vol. 14, pp. 242–247, 2014. [CrossRef] [PubMed] [Google Scholar]
- H. Woolhouse, S. Brown, A. Krastev, S. Perlen, and J. Gunn, Seeking help for anxiety and depression after childbirth: Results of the Maternal Health Study, Arch. Womens. Ment. Health, vol. 12, no. 2, pp. 75–83, 2009. [CrossRef] [PubMed] [Google Scholar]
- S. Suryono, J.E. Suseno, C. Mashuri, A. D. Sabila, J.A.M. Nugraha, M.H. Primasiwi, RFID Sensor for Automated Prediction of Reorder Point (ROP) Values in a Vendor Management Inventory (VMI) System Using Fuzzy Time Series, American Scientific Publishers., Vol. 23, 2398–2400, 2017. [Google Scholar]
- C. Colak, E. Karaman, and M. G. Turtay, Application of knowledge discovery process on the prediction of stroke, Comput Methods Programs Biomed, vol. 119, no. 3, pp. 181–185, 2015. [CrossRef] [PubMed] [Google Scholar]
- V. Karthikeyani, I. P. Begum, K. Tajudin, and I. S. Begam, Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Prediction, Int. J. Comput. Appl., vol. 60, no. 12, pp. 26–31, 2012. [Google Scholar]
- S. Sathyadevan and R. R. Nair, Comparative analysis of decision tree algorithms: Id3, c4.5 and random forest, Smart Innov. Syst. Technol., vol. 31, pp. 549–562, 2015. [CrossRef] [Google Scholar]
- F. S. Khan, R. M. Anwer, O. Torgersson, and G. Falkman, Data mining in oral medicine using decision trees, World Acad. Sci. Eng. Technol., vol. 37, pp. 225–230, 2008. [Google Scholar]
- D. T. Larose, Data Mining Methods and Models. 2006. [Google Scholar]
- H. Faller, Sensitivity, specificity, positive and negative predictive value, Rehabilitation (Stuttg)., vol. 44, no. 1, pp. 44–9, 2005. [CrossRef] [PubMed] [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.