[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124298-en":3,"doc-seo-124298-105":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},124298,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms","Machine learning algorithms are widely used to predict malaria risk and severity, discover immunity biomarkers for vaccine candidates, and identify molecular markers of antimalarial drug resistance. Building reliable models for new individuals requires large training datasets to maintain accuracy. Learning curves evaluate how predictive performance changes across different training dataset sizes. A tutorial demonstrates generating and interpreting learning curves for malaria prediction models across machine learning algorithms, while noting construction depends on existing data.","Zaloumis etal. Malaria Journal (2025) 24:242 [https://doi.org/10.1186/s12936-025-05479-3](https://doi.org/10.1186/s12936-025-05479-3)  \nMalaria Journal  \n METHODOLOGY Open Access  \nHow to use learning curves to evaluate  \nthe sample size for malaria prediction models developed using machine learning algorithms  \nSophie G. Zaloumis 1,2*, Megha Rajasekhar 1 and Julie A. Simpson 1,2,3  \nAbstract  \nBackground Machine learning algorithms have been used to predict malaria risk and severity, identify immunity biomarkers for malaria vaccine candidates, and determine molecular biomarkers of antimalarial drug resistance. Developing these prediction models requires large training datasets to ensure prediction accuracy when applied to new individuals in the target population. Learning curves can be used to assess the sample size required for the training dataset by evaluating the predictive performance of a model trained using different dataset sizes. These curves are agnostic to the specific prediction model, but their construction does require existing data. This tutorial demonstrateshow to generate and interpret learning curves for malaria prediction models developed using machine learning algorithms.  \nMethods To illustrate the approach, training dataset sizes were evaluated to inform the design of a “mock” prediction modelling study aimed to predict the artemisinin resistance status of Plasmodium falciparum malaria isolates from gene expression data. Data were simulated based on a previously published in vivo parasite gene expression dataset, which contained transcriptomes of 1043 P. falciparum isolates from patients with acute malaria, of which 29%(299/1043) were from slow clearing infections (parasite clearance half-life > 5 h) . Learning curves were produced for two machine learning algorithms, sparse Partial Least Squares-Discriminant Analysis plus Support Vector Machines (sPLSDA + SVMs) and random forests. Prediction error was measured using the balanced error rate (average of percentage of slow clearing infections incorrectly predicted as fast and percentage of fast clearing infections predicted as slow) .  \nResults For this mock malaria prediction study, the balanced error rate on a test dataset not used for model training (208 samples) was 50% for sPLSDA + SVMs and 50% for random forests on the smallest training dataset evaluated (20 samples) and 14% for sPLSDA + SVMs and 22% for random forests on the largest training dataset evaluated (835 samples) . The shape of the learning curves indicates that increasing the training dataset size beyond 835 samples is unlikely to significantly reduce the balanced error rates further.  \nConclusions Learning curves are a simple tool that can be used to determine the minimum sample size required for future prediction modelling studies of different malaria outcomes that use machine learning algorithms for prediction. These curves need to be generated for each specific prediction modelling application.  \nKeywords Machine learning, Sample size, Prediction modelling, Learning curves, Malaria, Transcriptomics  \n*Correspondence:  \nSophie G. Zaloumis  \n[sophiez@unimelb.edu.au](sophiez@unimelb.edu.au)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to o","cbCaivDeomqiW3Y5","https://ap.wps.com/l/cbCaivDeomqiW3Y5","pdf",1838575,1,11,"English","en",105,"# Background\n## Machine learning in malaria prediction\n## Need for priori sample size assessment\n# Methods\n## Simulated mock prediction modelling study design\n## Learning curve construction and evaluation metric\n# Results\n## Balanced error rate across training dataset sizes\n## Learning curve interpretation\n# Conclusions\n## Minimum sample size for future malaria prediction studies","[{\"question\":\"Why are learning curves useful for malaria prediction model development?\",\"answer\":\"Learning curves quantify how predictive performance changes as training dataset size varies, helping determine the minimum sample size needed for future malaria prediction modelling. They are agnostic to the specific prediction model but require existing data to construct.\"},{\"question\":\"How were training dataset sizes evaluated in the illustrative approach?\",\"answer\":\"Training dataset sizes were varied in a mock modelling study to predict artemisinin resistance status using gene expression–based transcriptomic data. Data were simulated from a previously published in vivo dataset.\"},{\"question\":\"What did the learning curve results suggest about increasing dataset size beyond a threshold?\",\"answer\":\"In the mock study, balanced error rate decreased as training size increased, and the learning curve shape indicated that expanding beyond 835 samples was unlikely to further significantly reduce balanced error rates.\"}]","How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms | PDF",1785821451,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"how-to-use-learning-curves-to-evaluate-the-sample-size-for-malaria-prediction-models-developed-using-machine-learning-algorithms","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/how-to-use-learning-curves-to-evaluate-the-sample-size-for-malaria-prediction-models-developed-using-machine-learning-algorithms/124298/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are learning curves useful for malaria prediction model development?","Question",{"text":75,"@type":76},"Learning curves quantify how predictive performance changes as training dataset size varies, helping determine the minimum sample size needed for future malaria prediction modelling. They are agnostic to the specific prediction model but require existing data to construct.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were training dataset sizes evaluated in the illustrative approach?",{"text":80,"@type":76},"Training dataset sizes were varied in a mock modelling study to predict artemisinin resistance status using gene expression–based transcriptomic data. Data were simulated from a previously published in vivo dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the learning curve results suggest about increasing dataset size beyond a threshold?",{"text":84,"@type":76},"In the mock study, balanced error rate decreased as training size increased, and the learning curve shape indicated that expanding beyond 835 samples was unlikely to further significantly reduce balanced error rates.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]