On the Clinical Use of Artificial Intelligence and Haematological Measurements for a Rapid Diagnosis and Care of Paediatric Malaria Patients in West Africa †.

Malaria continues to be a major cause of death worldwide, with a broad range of people spread over 90 countries being at risk of contracting the disease, and a significant cause of death in children under the age of 5. Due to this, there continues to be substantial investment towards not just the treatment of the disease, but also a more rapid and accurate means towards its diagnosis. 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 utilize patient blood measurements acquired from paediatric patients from Ghana, West Africa, alongside various configurations of AI models towards distinguishing between malaria vs. non-malaria cases in a sample set comprising over 2000 patients. Class balancing algorithms are utilized to first balance the classes for the various patient groups, followed by the use of AI algorithms to train machine learning models to differentiate between a malaria vs. a non-malaria patient. The results showcased a generally high prediction accuracy, especially in the case of models with nonlinear decision boundaries, therein showing how the proposed analytic pipeline can serve as a high-throughput approach towards tackling the malaria epidemic from a diagnostics perspective and ultimately enhancing patient care strategies. [ABSTRACT FROM AUTHOR]