[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128259-en":3,"doc-seo-128259-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128259,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",8,"Research & Report","Development of a pig wean-quality score using machine-learning algorithms to characterize and classify groups with high mortality risk under field conditions","Mortality during the post-weaning phase is a critical indicator of swine production performance, shaped by multiple epidemiological factors. Using retrospective data from 1,723 pig groups marketed in a US production system, the study builds a Wean-Quality Score (WQS) via machine learning. Three models are evaluated—Random Forest, Support Vector Machine, and Gradient Boosting Machine—for classifying high versus low 60-day mortality groups. Random Forest achieves the highest performance (accuracy 0.90, sensitivity 0.84, specificity 0.92) and identifies key predictors including pre-weaning mortality, weaning age, sow farm average parity, and PRRS status, plus stocking density and time to fill the barn. The WQS correlates with actual 60-day mortality (r=0.74) and supports targeted producer decisions before weaning.","Version of Record: [https://www.sciencedirect.com/science/article/pii/S0167587724002137](https://www.sciencedirect.com/science/article/pii/S0167587724002137)[ ](https://www.sciencedirect.com/science/article/pii/S0167587724002137)[Manuscript_174f44873707df4837933d34736668d6](Manuscript_174f44873707df4837933d34736668d6)  \nDevelopment of a pig wean-quality score using machine-learning algorithms to characterize and classify groups with high mortality risk under field conditions  \nEdison S. Magalhães 1, Danyang Zhang2, Cesar A. A. Moura3, Giovani Trevisan 1, Derald J. Holtkamp 1, Will A. López4, Chong Wang1, 2, Daniel C. L. Linhares 1, Gustavo S. Silva 1 *  \n1 Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, Iowa;  \n2 Department of Statistics, College of Liberal Arts and Sciences, Iowa State University, Ames, Iowa;  \n3 Iowa Select Farms, Iowa Falls, Iowa  \n4 Pig Improvement Company (PIC), Hendersonville, TN, USA.  \n*Corresponding author  \nGustavo S. Silva  \n2221 Lloyd, 1809 S Riverside Drive  \nDepartment of Veterinary Diagnostic and Production Animal Medicine Iowa State University, Ames, Iowa 50011  \n[gustavos@iastate.edu](gustavos@iastate.edu) (GSS)  \n© 2024 published by Elsevier. This manuscript is made available under the Elsevier user license  \n[https://www.elsevier.com/open-access/userlicense/1.0/](https://www.elsevier.com/open-access/userlicense/1.0/)  \n1  \n1 Development of a pig wean-quality score using machine-learning  \n2 algorithms to characterize and classify groups with high mortality  \n3 risk under field conditions  \n4  \n5 Author names and information were deidentified as per PVM instructions 6  \n2  \n7 Abstract  \n8 Mortality during the post-weaning phase is a critical indicator of swine production system  \n9 performance, influenced by a complex interaction of multiple factors of the epidemiological  \n10 triad. This study leveraged retrospective data from 1,723 groups of pigs marketed within a US  \n11 swine production system to develop a Wean-Quality Score (WQS) using machine learning  \n12 techniques. The study evaluated three machine learning models, Random Forest, Support Vector  \n13 Machine, and Gradient Boosting Machine, to classify groups having high or low 60-day  \n14 mortality, where high mortality groups represented 25% of the groups among the study  \n15 population with the highest mortality values (n=431; 60-day mortality=9.98%), and the  \n16 remaining 75% of the groups were of low mortality (n=1,292; 60-day mortality=2.75%) . The  \n17 best-performing model, Random Forest (RF), outperformed the other ML models in terms of  \n18 accuracy (0.90), sensitivity (0.84), and specificity (0.92) metrics, and was then selected for  \n19 further analysis, which consisted of creating the WQS and ranking the most important factors for  \n20 classifying groups as high or low mortality. The most important factors ranked through the RF  \n21 model to classify groups as high with high mortality were pre-weaning mortality, weaning age, 22 average parity of litters in sow farms, and PRRS status. Additionally, stocking conditions such as  \n23 stocking density and time to fill the barn were important predictors of high mortality. The WQS  \n24 was developed and correlated (r = 0.74) with the actual 60-day mortality of the groups, offering a  \n25 valuable tool for assessing post-weaning survivability in swine production systems before  \n26 weaning. This study highlights the potential of machine learning and comprehensive data  \n27 utilization to improve the assessment and management of weaned pig quality in commercial  \n28 swine production, which producers can utilize to identify and intervene in groups, according to  \n29 the WQS.  \n3  \n30 Keywords: swine; wean-quality; 60-day mortality; machine-learning; classification.  \n4  \n31 Introduction  \n32 The swine industry is pivotal in the ever-growing global demand for high-quality protein  \n33 (Mote and Rothschi","cbCaimqpxyHADXP8","https://ap.wps.com/l/cbCaimqpxyHADXP8","pdf",618351,4,1,35,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Post-weaning mortality as a sustainability indicator\n## Factors and the need for a comprehensive characterization\n## Importance of weaned pig quality and pre-weaning risk factors","[{\"question\":\"What is the Wean-Quality Score (WQS) and what problem does it address?\",\"answer\":\"The WQS is a machine-learning-derived score designed to assess post-weaning survivability. It helps characterize and classify pig groups by their risk of high 60-day mortality under field conditions.\"},{\"question\":\"Which machine learning models were tested, and how did Random Forest perform?\",\"answer\":\"Random Forest, Support Vector Machine, and Gradient Boosting Machine were evaluated for classifying high versus low 60-day mortality groups. Random Forest performed best with accuracy 0.90, sensitivity 0.84, and specificity 0.92.\"},{\"question\":\"Which factors were identified as most important for predicting high mortality groups?\",\"answer\":\"The Random Forest model ranked pre-weaning mortality, weaning age, average parity of litters in sow farms, and PRRS status as most important. Stocking conditions such as stocking density and time to fill the barn also predicted high mortality.\"}]","Development of a pig wean-quality score using machine-learning algorithms to characterize and classify groups with high mortality risk under field conditions | PDF",1785946279,88,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"development-of-a-pig-wean-quality-score-using-machine-learning-algorithms-to-characterize-and-classify-groups-with-high-mortality-risk-under-field-conditions","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/development-of-a-pig-wean-quality-score-using-machine-learning-algorithms-to-characterize-and-classify-groups-with-high-mortality-risk-under-field-conditions/128259/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the Wean-Quality Score (WQS) and what problem does it address?","Question",{"text":76,"@type":77},"The WQS is a machine-learning-derived score designed to assess post-weaning survivability. It helps characterize and classify pig groups by their risk of high 60-day mortality under field conditions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models were tested, and how did Random Forest perform?",{"text":81,"@type":77},"Random Forest, Support Vector Machine, and Gradient Boosting Machine were evaluated for classifying high versus low 60-day mortality groups. Random Forest performed best with accuracy 0.90, sensitivity 0.84, and specificity 0.92.",{"name":83,"@type":74,"acceptedAnswer":84},"Which factors were identified as most important for predicting high mortality groups?",{"text":85,"@type":77},"The Random Forest model ranked pre-weaning mortality, weaning age, average parity of litters in sow farms, and PRRS status as most important. 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