Maternal mortality classification for health promotive in Dairi using machine learning approach

Reducing maternal mortality rate is a key concern of health promotion in developing countries or city face. The investigated and survey for maternal mortality had been done in Dairy City. There are 149 samples got from the survey directly in this area for 2017. In this study, we use a machine learni...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2020-05, Vol.851 (1), p.12055
Main Authors: Manik, Henry, Siregar, M. Fidel G., Kintoko Rochadi, R., Sudaryati, Etti, Yustina, Ida, Triyoga, Rika Subarniati
Format: Article
Language:English
Subjects:
Classification
Decision trees
Developing countries
Health promotion
LDCs
Machine learning
Maternal mortality
Mortality
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Summary:Reducing maternal mortality rate is a key concern of health promotion in developing countries or city face. The investigated and survey for maternal mortality had been done in Dairy City. There are 149 samples got from the survey directly in this area for 2017. In this study, we use a machine learning approach to train and test the data of maternal mortality. The aim of this study to classification maternal mortality in health promotion for reducing the maternal mortality rate in Dairi. The result of this study indicated the decision tree and Naïve Bayes are available to train and test the dataset. The accuracy of the decision tree of maternal mortality is 100 % and the Naïve Bayes model indicates 97.37 % of maternal mortality.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/851/1/012055