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

Henry Manik; M. Fidel G. Siregar; R. Kintoko Rochadi; Etti Sudaryati; Ida Yustina; Rika Subarniati Triyoga

doi:10.1088/1757-899X/851/1/012055

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

Henry Manik, M. Fidel G. Siregar, R. Kintoko Rochadi, Etti Sudaryati, Ida Yustina, Rika Subarniati Triyoga

Research output: Contribution to journal › Conference article › peer-review

4 Citations (Scopus)

Abstract

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.

Original language	English
Article number	012055
Journal	IOP Conference Series: Materials Science and Engineering
Volume	851
Issue number	1
DOIs	https://doi.org/10.1088/1757-899X/851/1/012055
Publication status	Published - 28 May 2020
Externally published	Yes
Event	2020 International Conference on Information Technology and Engineering Management, ITEM 2020 - Batam, Indonesia Duration: 2 Apr 2020 → 4 Apr 2020

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10.1088/1757-899X/851/1/012055

Cite this

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title = "Maternal mortality classification for health promotive in Dairi using machine learning approach",

abstract = "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 Na{\"i}ve Bayes are available to train and test the dataset. The accuracy of the decision tree of maternal mortality is 100 % and the Na{\"i}ve Bayes model indicates 97.37 % of maternal mortality.",

author = "Henry Manik and Siregar, {M. Fidel G.} and {Kintoko Rochadi}, R. and Etti Sudaryati and Ida Yustina and Triyoga, {Rika Subarniati}",

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AU - Manik, Henry

AU - Siregar, M. Fidel G.

AU - Kintoko Rochadi, R.

AU - Sudaryati, Etti

AU - Yustina, Ida

AU - Triyoga, Rika Subarniati

N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2020/5/28

Y1 - 2020/5/28

N2 - 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.

AB - 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.

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DO - 10.1088/1757-899X/851/1/012055

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AN - SCOPUS:85086779634

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T2 - 2020 International Conference on Information Technology and Engineering Management, ITEM 2020

Y2 - 2 April 2020 through 4 April 2020

ER -

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

Abstract

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