[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124267-en":3,"doc-seo-124267-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124267,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning evidence towards eradication of malaria burden - A scoping review","Recent advancements show shallow and deep learning achieving high accuracies for malaria control and diagnosis, supporting evidence-based management, including economic evaluation. With the global malaria burden estimated at about 95%, eradication is framed as essential for meeting SDG targets on health and well-being. This scoping review covers 2018–2024 using PRISMA-ScR, drawing articles from ScienceDirect, PubMed, and Google Scholar, and analyzing 61 studies after screening. Findings indicate reduced shallow techniques but increasing deep learning as data volume and dimensionality grow, emphasizing real-time data and deployment for policy-ready recommendations.","Submitted: 2024-11-30 | Revised: 2025-01-06 | Accepted: 2025-01-20  \nKeywords: predictive systems, malaria burden, control, diagnosis, evidence-based recommendation  \nIdara JAMES 1* , Veronica OSUBOR 1  \n1 University of Benin, Nigeria, [idara.james@physci.uniben.edu](idara.james@physci.uniben.edu), [viosubor@yahoo.com](viosubor@yahoo.com)[ ](viosubor@yahoo.com)* Corresponding author: [idara.james@physci.uniben.edu](idara.james@physci.uniben.edu)  \nMachine learning evidence towards eradication of malaria burden: A scoping review  \nAbstract  \nRecent 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.  \n1. INTRODUCTION  \nIn recent time, machine learning algorithms are expanding the frontiers of modern computing applications. Despite being a computationally intensive model that relies on complex algorithms, it provides a veritable software tool for analyses of complex problems embedded in large data (Sarker, 2021) . Being a subfield of Artificial intelligence (AI) (Helm et al., 2020; Joshi, 2020), its performance is driven by the availability of voluminous but structured data for meaningful training and testing (otherwise referred to as learning process) of its model, without explicitly being programmed for the task (Lestarini et al., 2018) as obtained in the rulebased approach. The process of model learning from the intrinsic patterns associated with historical data is a characteristic feature of AI's model that possesses an inert ability to replicate human cognitive functions, such as learning and visual perception to predict the future (Qiu et al., 2016) . Apart from solving complex problems involving large data, its level of preciseness, transparency, and speed increases the chances of its adoption indifferent areas of application.  \nFor instance, the application of machine learning in healthcare offers remarkable opportunities to analyze daily health data to enhance patient care and timely diagnosis of disease (Mbunge & Batani, 2023; Fuhad et al., 2020) . Other areas of application of machine learning are education (Tiwari, 2023), government (Chen, 2022), agriculture (Sharma et al., 2021), transportation (Li & Xu, 2021), commerce (Liu, 2022), and more. Several machine learning approaches are defined based on the characteristic nature of the avail","cbCaidFDyLz9Kut5","https://ap.wps.com/l/cbCaidFDyLz9Kut5","pdf",686558,1,26,"English","en",105,"# Abstract\n# Introduction\n## Machine learning in modern computing\n## Applications in healthcare and beyond\n## Learning approaches and shallow vs deep learning\n## Malaria surveillance, control, diagnosis, and drug development\n## Machine learning evidence and decision-making\n## Workflow: data collection and preprocessing","[{\"question\":\"What is the main purpose of the scoping review?\",\"answer\":\"To map and summarize machine learning evidence related to malaria burden eradication, focusing on malaria control and diagnosis and how models inform evidence-based recommendations and policy development.\"},{\"question\":\"Which databases and time range were used to gather studies?\",\"answer\":\"Articles were retrieved for 2018–2024 from ScienceDirect, PubMed, and Google Scholar, then screened using PRISMA-ScR to reach a final set of 61 articles.\"},{\"question\":\"What trends were observed between shallow and deep learning research?\",\"answer\":\"Research using shallow machine learning for malaria control declined, while deep learning approaches showed steady growth, especially as data volume and dimensionality continue to increase.\"}]","Machine learning evidence towards eradication of malaria burden - 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