Implementation Data Mining using Decision Tree Method-Algorithm C4.5 for Postpartum Depression Diagnosis

Aris Supriyanto; Suryono Suryono; Jatmiko Endro Susesno

doi:10.1051/e3sconf/20187312012

All issues

Volume 73 (2018)

E3S Web Conf., 73 (2018) 12012

References

Open Access

Issue		E3S Web Conf. Volume 73, 2018 The 3^rd 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

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[1] 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]

[2] 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]

[3] 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]

[4] 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]

[5] 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]

[6] 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]

[7] 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]

[8] 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]

[9] 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]

[10] D. T. Larose, Data Mining Methods and Models. 2006. [Google Scholar]

[11] H. Faller, Sensitivity, specificity, positive and negative predictive value, Rehabilitation (Stuttg)., vol. 44, no. 1, pp. 44–9, 2005. [CrossRef] [PubMed] [Google Scholar]