Modelling Cases of Spontaneous Abortion Using Logistic Regression
International Journal of Data Science and Analysis
Volume 5, Issue 6, December 2019, Pages: 143-147
Received: Nov. 22, 2019; Accepted: Dec. 16, 2019; Published: Dec. 25, 2019
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Authors
Edwin Kung’u Kagereki, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Wanjoya, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Thomas Mageto, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
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Abstract
Spontaneous abortion is the expulsion of a foetus before the 28th week of gestation. Studies approximate that 10-25% of pregnancies are lost due to miscarriages. This phenomenon's aetiology remains a mystery hence uncertainty of detecting its cause. Furthermore, most pregnant women realize they have conceived later in the gestation period and some start antenatal care late during the pregnancy.In Kenya, total fertility rate has decreased for the last three decades from 8.1 to 3.9. However, with the decrease of total fertility rate, prevalence of maternal mortality and morbidity factors has greatly impacted on the pregnancy. Among them is spontaneous abortion. This study used secondary data from Kenyatta national hospital and employed logistic regression to model miscarriage's risk factors, investigate socio demographic and lifestyle factors, to investigate interactions among identified risk factors and fit a predictive model. Significant socio demographic factors identified were age and recurrent miscarriage. A woman who had experienced prior miscarriage had a 7.5-fold risk. Lifestyle factors identified were body mass index, diabetes mellitus and HIV. Underweight women had a 13.2-fold risk. There were significant interactions between gravidity and previous miscarriage; diabetes and body mass index. A predictive model was fit. The model has a good measure of separability, 80% classification accuracy and it is significant.
Keywords
Spontaneous Abortion, Logistic Regression, Risk Factor
To cite this article
Edwin Kung’u Kagereki, Anthony Wanjoya, Thomas Mageto, Modelling Cases of Spontaneous Abortion Using Logistic Regression, International Journal of Data Science and Analysis. Vol. 5, No. 6, 2019, pp. 143-147. doi: 10.11648/j.ijdsa.20190506.16
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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