Predicting the Utilization of Skilled Birth Attendant Among Antenatal Care Clients
| dc.contributor.advisor | Meshesha, Million (PhD) | |
| dc.contributor.author | Eshetu, Yishak | |
| dc.date.accessioned | 2019-04-23T10:56:42Z | |
| dc.date.accessioned | 2023-11-18T12:44:18Z | |
| dc.date.available | 2019-04-23T10:56:42Z | |
| dc.date.available | 2023-11-18T12:44:18Z | |
| dc.date.issued | 2018-02-05 | |
| dc.description.abstract | In Ethiopia maternal deaths are currently estimated at 412 per 100,000 live births and gets worst compared with other countries. It calls an alarm for Ethiopia to reduce such high maternal mortality rate by ensuring all birth has to be attended by skilled birth attendant (SBA). The majority of maternal deaths occur during labor, delivery, and the immediate postpartum period. Ethiopia is one of the countries with high maternal morbidity and mortality in sub-Saharan Africa which needs more public health care effort in the country. Hence the main objective of the study is to construct a predictive model for the utilization of skilled birth attendant among Antenatal Care clients. The study is guided by experimental research and followed Hybrid Data mining process model to achieve the goal of building predictive model. Andersen’s Health Behavioral Model was the basis for the assumption of this study to better understand the problem under study. The most recent data from the EDHS, 2016 is used and analyzed using data mining tools to predict the utilization of skilled birth attendant among ANC users. To build a model which can predict the utilization of skilled birth attendant among ANC users three data mining algorithms are used; J48 Decision tree, PART Rule Induction and Naïve Bayes. The data mining tool employed in this research is WEKA3.8.1. The experimented result shows that J48 decision tree outperforms by a classification accuracy of 98.74%. The most important rules generated from the datasets which surprised both domain and non-domain experts to predict the utilization of SBA among ANC users highly depend on the place of residence of the woman, place of region, house hold wealth index, husband highest level of education, and husband occupation. The experts suggested the applicability of the rules for both clinicians and as well as for policy makers. From this study it is observed that data mining techniques can effectively be used in the health sectors specially to predict the utilization of skilled birth attendant to avert the high maternal mortality rate in Ethiopia. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/18126 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Skilled Birth Attendance | en_US |
| dc.subject | Antenatal Care | en_US |
| dc.subject | Methodology | en_US |
| dc.subject | Understanding the Business Domain | en_US |
| dc.title | Predicting the Utilization of Skilled Birth Attendant Among Antenatal Care Clients | en_US |
| dc.type | Thesis | en_US |