Open AccessMaternal mortality classification for health promotive in Dairi using machine learning approach
Author(s) -
Henry M. Manik,
Muhammad Fidel Ganis Siregar,
R. Kintoko Rochadi,
Etti Sudaryati,
Ida Yustina,
Rika Subarniati Triyoga
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/851/1/012055
Subject(s) - naive bayes classifier , decision tree , mortality rate , bayes' theorem , promotion (chess) , medicine , machine learning , computer science , demography , artificial intelligence , bayesian probability , surgery , political science , politics , support vector machine , law , sociology
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.