Decision Making Model for Choosing Normal Maternity or Cesarean Section with Machine Learning Approach

Rimin, Ermi Girsang, Sri Lestari R. Nasution

Abstract

Globally, the number of cesarean section has almost doubled, namely 12% in 2000 to 21% in 2015. While more than 50 developing countries have cesarean birth rates> 27%. Normal childbirth actually has many advantages over cesarean section, but data in various hospitals shows an increase in cesarean section rates. The purpose of this study was to identify the relationship of factors that influence mothers with the decision to choose normal delivery or cesarean section. An evaluation of 3,121 respondents with 118 samples was conducted. Statistical evaluation using univariate and bivariate analysis with chi-square test, and multivariate analysis with multiple logistic regression at 95% confidence level ( = 0.05) was performed. Whereas the model of the relationship of the main factors in decision making in the selection of maternity scenarios was built using machine learning approach. Statistical evaluations indicate that there are only three variables (i.e., culture, lifestyle, and perception, p ≤ 0.009) that have a relationship with the decision of the mother to choose normal delivery or cesarean section. The factor with the greatest relation is perception (Exp (B) / OR was 3.305).

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Paper Citation


in Harvard Style

Rimin., Girsang E. and R. Nasution S. (2020). Decision Making Model for Choosing Normal Maternity or Cesarean Section with Machine Learning Approach.In Proceedings of the International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical - Volume 1: HIMBEP, ISBN 978-989-758-500-5, pages 88-95. DOI: 10.5220/0010289200880095


in Bibtex Style

@conference{himbep20,
author={Rimin and Ermi Girsang and Sri Lestari R. Nasution},
title={Decision Making Model for Choosing Normal Maternity or Cesarean Section with Machine Learning Approach},
booktitle={Proceedings of the International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical - Volume 1: HIMBEP,},
year={2020},
pages={88-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010289200880095},
isbn={978-989-758-500-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical - Volume 1: HIMBEP,
TI - Decision Making Model for Choosing Normal Maternity or Cesarean Section with Machine Learning Approach
SN - 978-989-758-500-5
AU - Rimin.
AU - Girsang E.
AU - R. Nasution S.
PY - 2020
SP - 88
EP - 95
DO - 10.5220/0010289200880095