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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/13001

Title: Analysis of Haematological Parameters as Predictors of Malaria Infection Using a Logistic Regression Model: A Case Study of a Hospital in the Ashanti Region of Ghana
Authors: Kobina Paintsil, Ellis
Yaw Omari-Sasu, Akoto
Glover Addo, Matthew
Akwasi Boateng, Maxwell
Issue Date: May-2019
Publisher: Malaria Research and Treatment
Citation: Malaria Research and Treatment, Volume 2019
Abstract: Malaria is the leading cause of morbidity in Ghana representing 40-60% of outpatient hospital attendance with about 10% ending up on admission. Microscopic examination of peripheral blood flm remains the most preferred and reliable method for malaria diagnosis worldwide. But the level of skills required for microscopic examination of peripheral blood flm is ofen lacking in Ghana. Tis study looked at determining the extent to which haematological parameters and demographic characteristics of patients could be used to predict malaria infection using logistic regression. Te overall prevalence of malaria in the study area was determined to be 25.96%; nonetheless, 45.30% of children between the ages of 5 and 14 tested positive. Te binary logistic model developed for this study identifed age, haemoglobin, platelet, and lymphocyte as the most signifcant predictors. Te sensitivity and specifcity of the model were 77.4% and 75.7%, respectively, with a PPV and NPV of 52.72% and 90.51%, respectively. Similar to RDT this logistic model when used will reduce the waiting time and improve the diagnosis of malaria.
Description: This article is published in Malaria Research and Treatment and also available at https://doi.org/10.1155/2019/1486370
URI: https://doi.org/10.1155/2019/1486370
http://hdl.handle.net/123456789/13001
Appears in Collections:College of Science

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