Research Article 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 Ellis Kobina Paintsil , 1 Akoto Yaw Omari-Sasu , 2 Matthew Glover Addo, 3 and Maxwell Akwasi Boateng 4 1 Kumasi Centre for Collaborative Research in Tropical Medicine, KNUST, Kumasi, Ghana 2 Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana 3 Department of Teoretical and Applied Biology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana 4 Faculty of Engineering, Ghana Technology University College, Ghana Correspondence should be addressed to Akoto Yaw Omari-Sasu; ayomari-sasu@knust.edu.gh Received 18 January 2019; Revised 16 April 2019; Accepted 22 April 2019; Published 21 May 2019 Academic Editor: Sasithon Pukrittayakamee Copyright © 2019 Ellis Kobina Paintsil et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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. 1. Introduction Apart from loss of valuable life, malaria is the leading cause of morbidity in Ghana representing 40-60% of outpatient hospital attendance with about 10% ending up on admission [1]. In the year 2002, it cost the government of Ghana US$ 50.05 million to control malaria; private businesses in Ghana also lost US$ 6.58 million in 2014 alone as a result of malaria [1, 2]. Te burden of malarial infections cannot be underestimated; it is widely agreed that malaria is a disease of the poor [3]. Terefore, it is not surprising that malaria is endemic in Africa where poverty is rampant [3]. Some clinicians think missing a case of malaria is a bigger problem than it is in reality and therefore treat most people with fever for malaria [4]. Overdiagnosis of malaria leads to wastage in the healthcare system; individual may have to pay more, spend more days at the hospital, and also miss work [5]. Due to the socioeconomic implications of misdiagnosis of malaria, it is imperative that investment is made in the accurate diagnosis and management of this disease to improve healthcare outcomes and reduce poverty [5]. In malaria diagnosis, RDTs are relatively simple and quick way of detecting the presence of the parasites in human blood. However, RDTs are expensive to use compared to malaria microscopy and may not be able to detect some infections with low parasitaemia [6]. Another weakness of RDT in the diagnosis of malaria is that it remains positive for about 15 days afer successful treatment of the disease [7]. Microscopic examination of peripheral blood flm remains the most pre- ferred and the gold standard for malaria diagnosis worldwide [8]. Nonetheless, the specifcity, sensitivity, and reliability of this method depend on the procedure for blood collection, Hindawi Malaria Research and Treatment Volume 2019, Article ID 1486370, 7 pages https://doi.org/10.1155/2019/1486370