USING ARTIFICIAL NEURAL NETWORKS FOR PREDICTING ABORTION CASES IN CHITUNGWIZA URBAN DISTRICT IN ZIMBABWE

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DR. SMARTSON. P. NYONI
MR. THABANI NYONI

Abstract

Abortion is an ancient and worldwide practice. Laws on abortion around the globe vary; in some countries, it is available to women upon request, while in others it is completely outlawed. In fact the liberalization of abortion is the subject of not only intense but also sensitive controversy, and once established, it is frequently challenged. Some defend access to abortion as a basic human right, a woman’s right, a sexual and reproductive right, and a right to health in light of the consequences of illegal abortions, while on the other side of the same coin; others seriously condemn it in the name of the embryo’s right to life. The current study used monthly time series data on abortion caseloads for Chitungwiza urban district from Janaury 2012 to December 2018, to predict abortion cases over the period January 2019 to December 2021. We applied the famous ANN (12, 12, 1) model. Residual analysis of the applied model indicates that the model is stable and acceptable. The results of the study reveal that abortion cases may generally be on an upwards trajectory in Chitungwiza urban district over the out-of-sample period. The study encourages the responsible public health authorities in Chitungwiza urban district to increase access to and improve post-abortion care in order to reduce maternal mortality.

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How to Cite
DR. SMARTSON. P. NYONI, & MR. THABANI NYONI. (2021). USING ARTIFICIAL NEURAL NETWORKS FOR PREDICTING ABORTION CASES IN CHITUNGWIZA URBAN DISTRICT IN ZIMBABWE. International Journal of Innovations in Engineering Research and Technology, 7(09), 151-155. https://repo.ijiert.org/index.php/ijiert/article/view/153
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Articles

How to Cite

DR. SMARTSON. P. NYONI, & MR. THABANI NYONI. (2021). USING ARTIFICIAL NEURAL NETWORKS FOR PREDICTING ABORTION CASES IN CHITUNGWIZA URBAN DISTRICT IN ZIMBABWE. International Journal of Innovations in Engineering Research and Technology, 7(09), 151-155. https://repo.ijiert.org/index.php/ijiert/article/view/153

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