Biru, Tesfaye (PhD)Teferri, Dereje (PhD)Anagaw, Shegaw2020-06-182023-11-182020-06-182023-11-182002-06http://etd.aau.edu.et/handle/12345678/21772Traditionally, very simple statistical techniques are used in the analysis of epidemiological studies. The predominant technique is logistic regression, in which the effects predictors are linear. However, because of their simplicity, i.t is difficult to use these models to discover unanticipated complex relationships, i.e., non-linearities in the effect of a predictor or interactions between predictors. Specifically, as the volume qj data increases, the traditional methods will become inefficient and impractical. This in turn calls the application of new methods and tools that can help to search large quantities of epidemiological data and to discover new patterns and relationships that are hidden in the data. Recently, to address the problem of identifying useful information and knowledge to support primary healthcare prevention and control activities, health care institutions are employing the data mining approach which uses more flexible models, such as, neural networks and decision trees, to discover unanticipated features from large volumes of data stored in epidemiological databases.Particularly, in the developed world, data mining technology has enabled health care institutions to identify and search previously unknown, actionable information from large health care databases and to apply it to improve the quality and efficiency of primary health care prevention and control activities. However, to the knowledge of the researcher, no health care institution in Ethiopia has used this state of the art technology to support health care decision-making.Thus, this research work has investigated the potential applicability of data mining technology to predict the risk of child mortality based up on community-based epidemiological datasets gathered by the BRHP epidemiological study. The methodology used for this research had three basic steps. These were collecting of data, data preparation and model building and testing. The required data was selected and extracted from the ten yea rs surveillance dataset of the BRHP epidemiological study. Then, data preparation tasks (such as data transformation, deriving of new fields, and handling of missing variables) were undertaken. Neural network and decision tree data mining techniques were employed to build and test the models. Models were built and tested by using a sample dataset of 1100 records of both alive and Died children.Several neural network and decision tree models were built and tested for their classification accuracy and many models with encouraging results were obtained. The two data mining methods used in this research work have proved to yield comparably sufficient results for practical use as far as misclassification rates come into consideration. However, unlike the neural network models, the results obtained by using the decision tree approach provided simple rules that can be used by nontechnical health care professionals to identify cases for which the rule is applicable.In this research work, the researcher has proved that an epidemiological database could be successfully mined to identify public health and sociology-demographic determinants (risk factors) that are associated with infant and child mortality in rural communitiesenInformation ScienceThesis