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Uncovering Knowledge That Supports Malaria Prevention and Control Intervention Program in Ethiopia

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dc.contributor.advisor Meshesa, Million (PhD)
dc.contributor.advisor Deressa, Wakgari (PhD)
dc.contributor.author Sahle, Geletaw
dc.date.accessioned 2018-11-28T07:50:48Z
dc.date.available 2018-11-28T07:50:48Z
dc.date.issued 2011-06
dc.identifier.uri http://etd.aau.edu.et/handle/123456789/14594
dc.description.abstract Malaria is one of the leading causes of death in Ethiopia. Though there are many efforts to control malaria, the complexity of the problems is still very severe. So there is a need to investigate in detail the synergic effect of risk factors with temperature, altitude, type of visit and malaria type and their causes of death. Hence in this research an attempt is made to determine the hierarchical importance of different risk factors and their patterns on malaria death occurrence. In this study, knowledge discovery techniques are evaluated to support and uncover knowledge to scale up the malaria prevention and intervention program in Ethiopia. CRISP methodology with classification algorithms such as J48, JRip and MLP and pattern discovery analysis techniques like Apriori adopted to uncover knowledge for the mining of interesting rule from total datasets of 37, 609 records. An attempt is made to preprocess the data using business and data understanding with detail statistical summary in order to fill missing and detect noisy value. Essential target dataset attributes have been constructed by integrating WHO malaria databases, National Metrological data and National Mapping Data. All classification techniques discover important attributes/factors that determine the malaria type of cases and occurrence of deaths. J48 Decision tree and MLP correctly classify 95.9% and 97.4%, respectively to predict occurrence of death. The findings of this rsearch indicate that rainfall is the significant factor that determines the prevalence of malaria. When the number of malaria cases increases there is a probability of death occurrences; the risk is relatively high with those less than 5 years age. In most zones, malaria transmission rate is high from May to January because of favorable climate conditions for malaria reproduction. Apriori techniques (both general and class association mining) also strengthen the result of J48 and MLP. More interestingly, it discovers occurrence of death, mostly related with severe anemia cases rather than pregnancy cases. Such interesting rule needs further investigation to validate. en_US
dc.language.iso en en_US
dc.publisher Addis Ababa University en_US
dc.subject Uncovering Knowledge That Supports Malaria Prevention en_US
dc.title Uncovering Knowledge That Supports Malaria Prevention and Control Intervention Program in Ethiopia en_US
dc.type Thesis en_US


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