首页|Ghent University Reports Findings in Machine Learning (Machine learning modeling to predict causes of infectious abortions and perinatal mortalities in cattle)
Ghent University Reports Findings in Machine Learning (Machine learning modeling to predict causes of infectious abortions and perinatal mortalities in cattle)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of Merelbeke, Belgium, by NewsRx editors, research stated, "A plethora of infectious and non-infectious c auses of bovine abortions and perinatal mortalities (APM) have been reported in literature. However, due to financial limitations or a potential zoonotic impact , many laboratories only offer a standard analytical panel, limited to a preesta blished number of pathogens." Our news journalists obtained a quote from the research from Ghent University, " To improve the cost-efficiency of laboratory diagnostics, it could be beneficial to design a targeted analytical approach for APM cases, based on maternal and e nvironmental characteristics associated with the prevalence of specific abortifa cient pathogens. The objective of this retrospective observational study was to implement a machine learning pipeline (MLP) to predict maternal and environmenta l factors associated with infectious APM. Our MLP based on a greedy ensemble app roach incorporated a standard tuning grid of four models, applied on a dataset o f 1590 APM cases with a positive diagnosis that was achieved by analyzing an ext ensive set of abortifacient pathogens. Production type (dairy/beef), gestation l ength, and season were successfully predicted by the greedy ensemble, with a mod est prediction capacity which ranged between 63 and 73 %. Besides t he predictive accuracy of individual variables, our MLP hierarchically identifie d predictor importance causes of associated environmental/maternal characteristi cs of APM. For instance, in APM cases that happened in beef cows, season at APM (spring/summer) was the most important predictor with a relative importance of 2 4 %. Furthermore, at the last trimester of gestation Trueperella py ogenes and Neospora caninum were the most important predictors of APM with a rel ative importance of 22 and 17 %, respectively. Interestingly, herd size came out as the most relevant predictor for APM in multiparous dams, with a relative importance of 12 %."
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