Estimating Poultry Production Mortality Exposed To Heat Wave Using Data Mining

Heat waves usually result in losses in animal production as the animals are exposed to thermal stress, inducing an increase in mortality and consequent economic losses. Animal science and meteorological databases from recent years contain enough data in the poultry production business to allow for modeling mortality losses due to heat wave incidence. This research aimed at analyzing a database of broiler production associated with climatic data using data mining techniques, such as attribute selection and data classification (decision tree) to model the impact of heat wave incidence on broiler mortality. The temperature and humidity index (THI) was used for screening environmental data. The data mining techniques allowed the development of three comprehensible models for predicting specifically high mortality in broiler production. Two models showed a classification accuracy of 89.3% by using Principal Component Analysis and Wrapper feature selection approaches. Both models obtained a class precision of 0.83 for classifying high mortality. When the feature selection was made by the domain experts, the model accuracy reached 85.7%, while the class precision for high mortality was 0.76. Meteorological data and the calculated THI from meteorological stations were helpful to select the range of harmful environmental conditions for broilers at 29 and 42 days old. The data mining techniques were useful for building animal production models.865872Abaurrea, J., Asin, J., Cebrian, A.C., Centelles, A., On the need of a changing threshold in heat wave definition (2006) Geog. Res. Abstract, 8, pp. 762-775Breiman.L. 1996. Bagging predictors. Machine Learning. 26:123-140Chapman, P.Clinton, J.Kerber, R.Khabaza, T.Reinartz, T.Shearer, CWirth, R. CRISP-DM 1.0step-by-step data mining guide. 2000. 78p. Available at: http://www.crisp-dm.org/CRISPWP-0800.pdf. Accessed 31 October 2005. Chepete, H.J.Chimbombi, E.Tsheko, R. 2005. 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The association of ACE gene D/I polymorphism with cardiovascular risk factors in a population from Rio de Janeiro

Our aim was to determine the frequencies of the angiotensin-converting enzyme (ACE) gene alleles D and I and any associations to cardiovascular risk factors in a population sample from Rio de Janeiro, Brazil. Eighty-four adults were selected consecutively during a 6-month period from a cohort subgroup of a previous large cross-sectional survey in Rio de Janeiro. Anthropometric data and blood pressure measurements, echocardiogram, albuminuria, glycemia, lipid profile, and ACE genotype and serum enzyme activity were determined. The frequency of the ACE*D and I alleles in the population under study, determined by PCR, was 0.59 and 0.41, respectively, and the frequencies of the DD, DI, and II genotypes were 0.33, 0.51, and 0.16, respectively. No association between hypertension and genotype was detected using the Kruskal-Wallis method. Mean plasma ACE activity (U/mL) in the DD (N = 28), DI (N = 45) and II (N = 13) groups was 43 (in males) and 52 (in females), 37 and 39, and 22 and 27, respectively; mean microalbuminuria (mg/dL) was 1.41 and 1.6, 0.85 and 0.9, and 0.6 and 0.63, respectively; mean HDL cholesterol (mg/dL) was 40 and 43, 37 and 45, and 41 and 49, respectively, and mean glucose (mg/dL) was 93 and 108, 107 and 98, and 85 and 124, respectively. A high level of ACE activity and albuminuria, and a low level of HDL cholesterol and glucose, were found to be associated with the DD genotype. Finally, the II genotype was found to be associated with variables related to glucose intolerance