Machine learning evidence towards eradication of malaria burden: A scoping review - JAMES, Idara; OSUBOR, Veronica (Applied Computer Science, 2025)
Recent advancements have shown that shallow and deep learning models achieve impressive performance accuracies of over 97% and 98%, respectively, in providing precise evidence for malaria control and diagnosis. This effectiveness highlights the importance of these models in enhancing our understanding of malaria management, which includes critical areas such as malaria control, diagnosis and the economic evaluation of the malaria burden. By leveraging predictive systems and models, significant opportunities for eradicating malaria, empowering informed decision-making and facilitating the development of effective policies could be established. However, as the global malaria burden is approximated at 95%, there is a pressing need for its eradication to facilitate the achievement of SDG targets related to good health and well-being. This paper presents a scoping review covering the years 2018 to 2024, utilizing the PRISMA-ScR protocol, with articles retrieved from three scholarly databases: Science Direct (9%), PubMed (41%), and Google Scholar (50%). After applying the exclusion and inclusion criteria, a final list of 61 articles was extracted for review. The results reveal a decline in research on shallow machine learning techniques for malaria control, while a steady increase in deep learning approaches has been noted, particularly as the volume and dimensionality of data continue to grow. In conclusion, there is a clear need to utilize machine learning algorithms through real-time data collection, model development, and deployment for evidence-based recommendations in effective malaria control and diagnosis. Future research directions should focus on standardized methodologies to effectively investigate both shallow and deep learning models.