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dc.contributor.authorKibui, John
dc.date.accessioned2016-07-18T07:43:38Z
dc.date.available2016-07-18T07:43:38Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/11071/4618
dc.descriptionSubmitted in partial fulfillment of the requirements for the Degree of Masters of Science in Information Technologyen_US
dc.description.abstractCases of type 2 diabetes have been on the increase in the last ten years and this has caused many deaths and untold suffering to victims of the disease. One of the main challenges in combating the disease is in the education of the population on the causes and risk factors associated with diabetes including lifestyle habits and how to keep the disease at bay. Early detection of the disease has also been a key necessity in order to tackle the disease and its prevalence with a vision to slowing down the spread of the disease. Consequently, the aim of this study is to come up with an early detection diabetes model. This shall be basis the various risks factors that are associated with the disease and be able to provide advice to the patient depending on an individual’s detected symptoms. The model shall be useful in the insurance sector to provide a guide at determining insurance medical premiums basis an individual exhibiting signs of early diabetes. Depending on the stage of the disease, this information shall be used by health insurance companies to determine one’s insurance premiums. Hence, this solution shall be used to assist insurance companies on how to structure their health schemes for their clients. Ideally, the solution shall work by receiving user input parameters pertaining to their health such as but not limited to their weight, height and lifestyle. These factors are compiled and depending on the input, the output generated gives an indication as to whether one is in the early stages of type 2 diabetes.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectDiabetesen_US
dc.subjectMobile appen_US
dc.subjectKenyaen_US
dc.subjectICTen_US
dc.subjectMobileen_US
dc.subjectHealthen_US
dc.titleMobile based expert application for the early detection of diabetes in Kenyaen_US
dc.typeThesisen_US


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