{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/106401"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/106401","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Impedance spectroscopy for the characterization of swine reproductive states","abstract":"This study uses machine learning to examine data acquired from a frequency sweeping electrical impedance spectrometer taking readings from sows in order to potentially determine if the resultant data can be used to predict a sow's date of estrus, date of farrowing, and if the inseminated sow has become pregnant. The frequencies analyzed were between 1000 Hz and 29000 Hz. The models were logistic regressions which were reduced by a stepwise Akaike Information Criterion. The data set for the fertility model included 250 frequency sweeps across 85 animals, for farrowing there were 921 observations across 421 animals, and for the pregnancy data set there were 395 observations across 227 animals. The fertility and farrowing detection models displayed substantial strength, with the latter holding particular promise in the face of conventional methods. The fertility model had a high Area Under Curve (AUC) of 0.946, while the two tested farrowing models had an AUC of 0.839 and 0.834. Pregnancy detection models were skewed by the retrieved dataset containing few non-pregnant animals, with only 50 observations across 29 non-pregnant animals as compared to 345 observations across 198 pregnant animals, and had an AUC of 0.839. Separating between late and early readings in the model is considered to be a potential improvement to the pregnancy model, with an AUC of 0.939, although additional observations, particularly of non-pregnant animals, are necessary. The groundwork was laid for further work to be done, particularly regarding whether an animal that has failed to become pregnant is going to return to estrus. Future models that incorporate more information about individual animals such as the number of litters previously birthed, as well as expanding this concept into other animals, are recommended.","abstract_html":"This study uses machine learning to examine data acquired from a frequency sweeping electrical impedance spectrometer taking readings from sows in order to potentially determine if the resultant data can be used to predict a sow's date of estrus, date of farrowing, and if the inseminated sow has become pregnant. The frequencies analyzed were between 1000 Hz and 29000 Hz. The models were logistic regressions which were reduced by a stepwise Akaike Information Criterion. The data set for the fertility model included 250 frequency sweeps across 85 animals, for farrowing there were 921 observations across 421 animals, and for the pregnancy data set there were 395 observations across 227 animals. The fertility and farrowing detection models displayed substantial strength, with the latter holding particular promise in the face of conventional methods. The fertility model had a high Area Under Curve (AUC) of 0.946, while the two tested farrowing models had an AUC of 0.839 and 0.834. Pregnancy detection models were skewed by the retrieved dataset containing few non-pregnant animals, with only 50 observations across 29 non-pregnant animals as compared to 345 observations across 198 pregnant animals, and had an AUC of 0.839. Separating between late and early readings in the model is considered to be a potential improvement to the pregnancy model, with an AUC of 0.939, although additional observations, particularly of non-pregnant animals, are necessary. The groundwork was laid for further work to be done, particularly regarding whether an animal that has failed to become pregnant is going to return to estrus. Future models that incorporate more information about individual animals such as the number of litters previously birthed, as well as expanding this concept into other animals, are recommended.","abstract_has_math":false,"creators":["Glass, Daniel J."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Agricultural & Biological Engr","degree_department":null,"school":null,"contributors":["Bhalerao, Kaustubh","Grift, Tony","Knox, Robert V"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-03-02T22:18:20Z","date_published":"2020-03-02T22:18:20Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Machine Learning","Pig","Pigs","Swine","Electrical Impedance Spectroscopy","Impedance Spectroscopy","Farrowing","Estrus","Animal Pregnancy"],"languages":["en"],"rights":["Copyright 2019 Daniel J. 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The fertility and farrowing detection models displayed substantial strength, with the latter holding particular promise in the face of conventional methods. The fertility model had a high Area Under Curve (AUC) of 0.946, while the two tested farrowing models had an AUC of 0.839 and 0.834. Pregnancy detection models were skewed by the retrieved dataset containing few non-pregnant animals, with only 50 observations across 29 non-pregnant animals as compared to 345 observations across 198 pregnant animals, and had an AUC of 0.839. Separating between late and early readings in the model is considered to be a potential improvement to the pregnancy model, with an AUC of 0.939, although additional observations, particularly of non-pregnant animals, are necessary. The groundwork was laid for further work to be done, particularly regarding whether an animal that has failed to become pregnant is going to return to estrus. Future models that incorporate more information about individual animals such as the number of litters previously birthed, as well as expanding this concept into other animals, are recommended.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-12-01","The student, Daniel Glass, accepted the attached license on 2019-12-12 at 09:46.","The student, Daniel Glass, submitted this Thesis for approval on 2019-12-12 at 13:27.","This Thesis was approved for publication on 2019-12-12 at 17:19.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14809 on 2020-02-28 at 17:24:29","Made available in DSpace on 2020-03-02T22:18:20Z (GMT). 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The frequencies analyzed were between 1000 Hz and 29000 Hz. The models were logistic regressions which were reduced by a stepwise Akaike Information Criterion. The data set for the fertility model included 250 frequency sweeps across 85 animals, for farrowing there were 921 observations across 421 animals, and for the pregnancy data set there were 395 observations across 227 animals. The fertility and farrowing detection models displayed substantial strength, with the latter holding particular promise in the face of conventional methods. The fertility model had a high Area Under Curve (AUC) of 0.946, while the two tested farrowing models had an AUC of 0.839 and 0.834. Pregnancy detection models were skewed by the retrieved dataset containing few non-pregnant animals, with only 50 observations across 29 non-pregnant animals as compared to 345 observations across 198 pregnant animals, and had an AUC of 0.839. Separating between late and early readings in the model is considered to be a potential improvement to the pregnancy model, with an AUC of 0.939, although additional observations, particularly of non-pregnant animals, are necessary. The groundwork was laid for further work to be done, particularly regarding whether an animal that has failed to become pregnant is going to return to estrus. Future models that incorporate more information about individual animals such as the number of litters previously birthed, as well as expanding this concept into other animals, are recommended.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-12-01","The student, Daniel Glass, accepted the attached license on 2019-12-12 at 09:46.","The student, Daniel Glass, submitted this Thesis for approval on 2019-12-12 at 13:27.","This Thesis was approved for publication on 2019-12-12 at 17:19.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14809 on 2020-02-28 at 17:24:29","Made available in DSpace on 2020-03-02T22:18:20Z (GMT). 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