{"id":"https://openalex.org/W4415744221","doi":"https://doi.org/10.1109/qrs-c65679.2025.00094","title":"Predicting Equine Health Outcomes Using Machine Learning Models Trained on Clinical Indicators and Limited Behavioral Data","display_name":"Predicting Equine Health Outcomes Using Machine Learning Models Trained on Clinical Indicators and Limited Behavioral Data","publication_year":2025,"publication_date":"2025-07-16","ids":{"openalex":"https://openalex.org/W4415744221","doi":"https://doi.org/10.1109/qrs-c65679.2025.00094"},"language":null,"primary_location":{"id":"doi:10.1109/qrs-c65679.2025.00094","is_oa":false,"landing_page_url":"https://doi.org/10.1109/qrs-c65679.2025.00094","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 25th International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053276061","display_name":"Hong Zhang","orcid":"https://orcid.org/0000-0001-6776-9074"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]},{"id":"https://openalex.org/I4210157750","display_name":"Aurora College","ror":"https://ror.org/05bn6qf70","country_code":"CA","type":"education","lineage":["https://openalex.org/I4210157750"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Kaitlyn Song Zhang","raw_affiliation_strings":["The Country Day School,Aurora,Canada","The University of Waterloo,Waterloo,Canada"],"affiliations":[{"raw_affiliation_string":"The Country Day School,Aurora,Canada","institution_ids":["https://openalex.org/I4210157750"]},{"raw_affiliation_string":"The University of Waterloo,Waterloo,Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030668195","display_name":"Zijie Niu","orcid":"https://orcid.org/0000-0002-0769-9235"},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zijie Niu","raw_affiliation_strings":["Northwest A&#x0026;F University,Yangling,China"],"affiliations":[{"raw_affiliation_string":"Northwest A&#x0026;F University,Yangling,China","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022245524","display_name":"Kevin Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]},{"id":"https://openalex.org/I4210157750","display_name":"Aurora College","ror":"https://ror.org/05bn6qf70","country_code":"CA","type":"education","lineage":["https://openalex.org/I4210157750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Kevin Zhang","raw_affiliation_strings":["The Country Day School,Aurora,Canada","The University of Waterloo,Waterloo,Canada"],"affiliations":[{"raw_affiliation_string":"The Country Day School,Aurora,Canada","institution_ids":["https://openalex.org/I4210157750"]},{"raw_affiliation_string":"The University of Waterloo,Waterloo,Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5053276061"],"corresponding_institution_ids":["https://openalex.org/I151746483","https://openalex.org/I4210157750"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.4620915,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"734","last_page":"742"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10872","display_name":"Veterinary Equine Medical Research","score":0.6969000101089478,"subfield":{"id":"https://openalex.org/subfields/3402","display_name":"Equine"},"field":{"id":"https://openalex.org/fields/34","display_name":"Veterinary"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10872","display_name":"Veterinary Equine Medical Research","score":0.6969000101089478,"subfield":{"id":"https://openalex.org/subfields/3402","display_name":"Equine"},"field":{"id":"https://openalex.org/fields/34","display_name":"Veterinary"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12365","display_name":"Effects of Environmental Stressors on Livestock","score":0.08020000159740448,"subfield":{"id":"https://openalex.org/subfields/1103","display_name":"Animal Science and Zoology"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13036","display_name":"Veterinary Practice and Education Studies","score":0.0364999994635582,"subfield":{"id":"https://openalex.org/subfields/3616","display_name":"Speech and Hearing"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.7433000206947327},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5831000208854675},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5770000219345093},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.4699000120162964},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.46790000796318054},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.43720000982284546},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.42910000681877136},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.42590001225471497},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4196999967098236}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.8070999979972839},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7583000063896179},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.7433000206947327},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5831000208854675},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5770000219345093},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5267999768257141},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.4699000120162964},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.46790000796318054},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.43720000982284546},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.42910000681877136},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.42590001225471497},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4196999967098236},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4120999872684479},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.3637999892234802},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C5481197","wikidata":"https://www.wikidata.org/wiki/Q16766476","display_name":"Decision tree learning","level":3,"score":0.33410000801086426},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3231000006198883},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3012000024318695},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.26739999651908875},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2603999972343445}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/qrs-c65679.2025.00094","is_oa":false,"landing_page_url":"https://doi.org/10.1109/qrs-c65679.2025.00094","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 25th International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W4318814327","https://openalex.org/W4363650545","https://openalex.org/W4389485991","https://openalex.org/W4407416865"],"related_works":[],"abstract_inverted_index":{"The":[0,121],"capacity":[1],"to":[2,67,101,137,145,166,178,216,241],"perceive":[3],"and":[4,30,44,81,105,117,128,149,184,222,233,238],"anticipate":[5],"the":[6,48,72,89,134,138,211,231],"health":[7,65,69,200],"status":[8],"of":[9,15,60,140],"horses":[10],"is":[11],"a":[12,51,56,160,175,190],"critical":[13],"aspect":[14],"equine":[16,64],"veterinary":[17],"care.":[18],"Recent":[19],"studies":[20],"have":[21],"shown":[22],"that":[23],"machine":[24],"learning":[25,157],"algorithms":[26,73],"can":[27],"accurately":[28],"diagnose":[29],"classify":[31],"animal":[32],"diseases":[33],"based":[34],"on":[35,159,229],"physiological":[36,239],"signs.":[37],"Using":[38],"properties":[39],"like":[40],"heart":[41],"rate,":[42],"temperature,":[43],"other":[45],"clinical":[46,205],"parameters,":[47],"study":[49,209],"offers":[50],"classification":[52],"model":[53,122,177,243],"developed":[54],"from":[55],"publicly":[57],"available":[58],"dataset":[59,232],"more":[61,235],"than":[62],"2,000":[63],"records":[66],"predict":[68],"outcomes.":[70],"Among":[71,133],"tested,":[74],"including":[75],"k-Nearest":[76],"Neighbors":[77],"(KNN),":[78],"Decision":[79],"Tree,":[80],"Light":[82],"Gradient":[83],"Boosting":[84],"Machine":[85],"(LightGBM),":[86],"LightGBM":[87,141],"achieved":[88],"highest":[90],"validation":[91],"accuracy":[92],"at":[93],"approximately":[94],"76%.":[95],"Exploratory":[96],"data":[97,206,240],"analysis":[98],"was":[99,123],"conducted":[100],"visualize":[102],"feature":[103],"distributions":[104],"identify":[106],"correlations,":[107],"followed":[108],"by":[109],"preprocessing":[110],"steps":[111],"such":[112],"as":[113],"handling":[114],"missing":[115],"values":[116],"encoding":[118],"categorical":[119,147],"variables.":[120],"trained":[124],"using":[125,174],"five-fold":[126],"cross-validation":[127],"fine-tuned":[129],"for":[130,197,213],"optimal":[131],"performance.":[132],"factors":[135],"contributing":[136],"success":[139],"were":[142,172],"its":[143,150],"ability":[144],"handle":[146],"features":[148,188],"leaf-wise":[151],"tree":[152],"growth":[153],"strategy,":[154],"which":[155],"improved":[156],"efficiency":[158],"moderately":[161],"sized":[162],"dataset.":[163],"In":[164],"addition":[165],"structured":[167],"data,":[168],"limited":[169],"behavioral":[170,237],"descriptors":[171],"incorporated":[173],"language":[176],"provide":[179],"additional":[180],"context":[181],"regarding":[182],"stress":[183],"discomfort.":[185],"While":[186],"these":[187],"had":[189],"smaller":[191],"role,":[192],"they":[193],"created":[194],"new":[195],"opportunities":[196],"interpreting":[198],"subtle":[199],"cues":[201],"not":[202],"included":[203],"in":[204,219],"alone.":[207],"This":[208],"demonstrates":[210],"potential":[212],"predictive":[214],"modeling":[215],"assist":[217],"veterinarians":[218],"early":[220],"diagnosis":[221],"treatment":[223],"planning.":[224],"Future":[225],"work":[226],"may":[227],"focus":[228],"expanding":[230],"implementing":[234],"detailed":[236],"improve":[242],"generalization.":[244]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-31T00:00:00"}
