Predicting Infant Immunization Status in Ethiopian: The Case of Ethiopia Demographic and Health Survey 2011
| dc.contributor.advisor | Meshesha, Million (PhD) | |
| dc.contributor.advisor | Mekonnen, Wubegzier (PhD) | |
| dc.contributor.author | Abebe, Hiwot | |
| dc.date.accessioned | 2018-11-27T14:25:24Z | |
| dc.date.accessioned | 2023-11-29T04:56:53Z | |
| dc.date.available | 2018-11-27T14:25:24Z | |
| dc.date.available | 2023-11-29T04:56:53Z | |
| dc.date.issued | 2014-06 | |
| dc.description.abstract | Background: Immunization is one of the most cost effective and efficient interventions saving the lives of many millions of infants and children from dying of infectious and preventable diseases. In 2007, approximately 27 million infants are not vaccinated against common childhood diseases and 2–3 million children are dying annually from easily preventable diseases and many more fall ill. Objective: The research has a general objective of construct a predictive model using data mining technology that helps to predict the infants’ immunization status in Ethiopia. The result of the study is expected to be important for different parties such as infants, health professionals, policy makers, programmers and researchers. Methodology: This study is guided by a Hybrid-data mining model which is a six step knowledge discovery process model such as understanding of the problem, understanding of the data, preparation of the data, data mining, and evaluation of the discovered knowledge and use of the discovered knowledge. The study has used 8,210 instances, 12 predicting and one outcome variables to run the experiments. Due to the nature of the problem and attributes contained in the dataset, classification data mining task is selected to build the classifier models. The mining algorithms; J48 decision tree, sequence minimal optimization support vector machine, multilayer perceptron neural network and partial decision tree rule induction are used in all experiment due to their popularity in recent related works. Ten-fold cross validation technique is used to train and test the classifier models. Performance of the models is compared using accuracy, true positive rate, false positive rate, and the area under the Receiver Operating Characteristics curve. Result: The J48 decision tree has given the best classification and a better predictive accuracy of the infant immunization status in Ethiopia. The experiment has generated a model with accuracy of 62.5%, weighted precision of 62.5% and weighted ROC area of 67.6% for the J48 decision tree. And if place of delivery = home region = Affar AND mother-education-level = no-education AND wealth-status = poor AND listening-to-radio = not-at-all AND mother-age = 25-29 AND parity = 6-7 then Unimmunised (10.0/1.0).Therefore, increase awareness creation among women in pastoralist communities so as to enhance vaccine coverage. Conclusion: The results achieved from this research indicate that data mining is useful in bringing relevant information from large and complex EDHS dataset, and we can this information for predicting infant immunization status and decision making. The most important attributes that determine infant immunization status were place of delivery, region, mother's educational level, listening to radio, father education level, residence, mother age, wealth status, parity, distance to health facility and marital status. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/14574 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Predicting Infant Immunization Status in Ethiopian | en_US |
| dc.title | Predicting Infant Immunization Status in Ethiopian: The Case of Ethiopia Demographic and Health Survey 2011 | en_US |
| dc.type | Thesis | en_US |