Machine learning approaches classify clinical malaria outcomes based on haematological parameters

Background: Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI) remains a challenge. Furthermore, the success of rapid diagnostic tests (RDT) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitemia. Analysis of haematological indices can be used to support identification of possible malaria cases for further diagnosis, especially in travelers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM and severe malaria (SM) using haematological parameters.