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dc.contributor.authorMacharia, Georgina Wangeci
dc.date.accessioned2022-08-02T06:39:35Z
dc.date.available2022-08-02T06:39:35Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/11071/12914
dc.descriptionA Thesis Submitted to the School of Computing and Engineering Sciences in Partial Fulfilment for the Requirement of the Degree of Master of Science in Information Technology of Strathmore Universityen_US
dc.description.abstractMalaria is the most infectious disease and continues to be a major global health problem with part of the world’s population being at risk to various degrees of malaria risk. In many endemic countries, the clinical diagnosis has been proven to be the only method used to decide on the correct treatment even though the method is not that accurate and may be limited by the low specificity of the various signs and symptoms of malaria. Some of the challenges affecting the early detection of malaria include and are not limited to severe anaemic and respiratory diseases in children and delayed detection of malaria leading to irreversible and fatal complications in children. These challenges led to the implementation of a mobile application for early detection of malaria in children. Several measures have been made in combating malaria, however, the indicators in Africa still do not show any promise for elimination in the future as the infections still result in high mortality and a rise in the high rate of children affected with malaria. Reducing the number of deaths in children affected by malaria would yield huge gains in reducing the overall under five mortality and morbidity rates in malarial dominant areas. The purpose of this research is to be able to detect malaria at an early stage in children in the regions of Western Kenya. The solution proposed, was coming up with a mobile application that will facilitate the most suitable and convenient way of malaria disease detection, especially in rural and remote regions. The camera of the smartphone will act as a microscope and there will be no need to attach it to the eyepiece of the microscope. This enhances mobility and the Remote Health Worker is able to diagnose the patient and offer treatment. This will allow real time treatment and the records will be uploaded to the database for the next visit from the Remote Health Worker. The study used agile software development model to design, develop and test the application since it is iterative. The mobile malaria detection application was developed and tested to be used by the resulting model which had an accuracy level of 94%. The findings from the usability acceptance test showed that the users acknowledged that the application was easy to navigate, use and the instructions were clear to use as a first-time user.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectDetectionen_US
dc.subjectRespiratory diseasesen_US
dc.subjectMortalityen_US
dc.subjectMorbidityen_US
dc.titleMobile application for early detection of Malaria in children: case of Western Kenyaen_US
dc.typeThesisen_US


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