Explainable AI for enhanced accuracy in malaria diagnosis using ensemble machine learning models.

Journal: BMC medical informatics and decision making
PMID: 40217281

Abstract

BACKGROUND: Malaria, an infectious disease caused by protozoan parasites belonging to the Plasmodium genus, remains a significant public health challenge, with African regions bearing the heaviest burden. Machine learning techniques have shown great promise in improving the diagnosis of infectious diseases, such as malaria.

Authors

  • Olushina Olawale Awe
    Statistical Learning Lab, Federal University of Bahia, Salvador, Brazil. oawe@unicamp.br.
  • Peter Njoroge Mwangi
    Department of Data Science, African Institute for Mathematical Sciences (AIMS), Limbe, Cameroon.
  • Samuel Kotva Goudoungou
    Department of Data Science, African Institute for Mathematical Sciences (AIMS), Limbe, Cameroon.
  • Ruth Victoria Esho
    Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.
  • Olanrewaju Samuel Oyejide
    Department of Clinical Pharmacology and Clinical Pharmacy, Bogomolets National Medical University, Kiev, Ukraine.

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