| 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 | |
Implementation Data Mining using Decision Tree Method-Algorithm C4.5 for Postpartum Depression Diagnosis
1 Magister Program of Information System, School of Postgraduate Studies, Diponegoro University, Semarang - Indonesia
2 Departement of Physics, Science and Mathematics Faculty, Diponegoro University, Semarang - Indonesia
* Corresponding author: arissupriyanto67@gmail.com
Postpartum depression is a serious problem that needs to be addressed because it has negative effects on family, child welfare, cognitive, and mother child interactions. Diagnosis is done based on psychological condition, blood pressure, respiration, body temperature, and classification data extract by decision tree C4.5 algorithm method. Results of this study in the form of an online information system that can identify the level of depression more quickly and precisely. The results showed the greatest gain on the psychological variables of 0.57 node 1, blood pressure 0.54 node 2, body temperature 0,54 node 3, means that the three variables are more influential on the condition of depressed patients, and should be given priority treatment. Test results from 50 patients with 50 examinations showed 62% prevalence, 65.62% sensitivity, specificity 77.77%, negative predictive value of 56%, and positive predictive value 84%, and
Key words: postpartum depression / decision tree / algorithm C4.5 / online information system
© The Authors, published by EDP Sciences, 2018
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