Malaria continues to be a major cause of death worldwide with a broad range of people spread across over 90 countries being at risk towards suffering from the disease. Due to this, there continues to be a substantial amount of investments towards, not just the treatment of the disease but also-a more rapid and accurate means towards diagnosis of the disease. In this work, we look to explore how measurements obtained from the Complete Blood Count(CBC) technique from patients blood, alongside Artificial Intelligence(AI) methods could form an affordable analytical pipeline that could be adopted in hospital settings in both developed and developing countries.

As part of this work, we utilise patient bloods measurement acquired from Ghana, West Africa alongside various configurations of AI models towards distinguishing between severe malaria (SM), uncomplex malaria (UM) and non-malarial infections (nMI) in a sample set comprising over 2000 patients. From which it is believed that the results showcase how a combination of measurements and AI modelling can contribute towards tackling the malaria epidemic from a diagnostics perspective and ultimately enhancing patient care strategies.