A Computerized Mastitis Decision Aid Using Farm-Based Records: An Artificial Neural Network Approach

¹ Department of Dairy and Animal Science, The Pennsylvania State University, University Park 16802
² Department of Industrial Engineering, Kyungsung University, Pusan, Korea
³ Veterinary Medical Teaching and Research Center, University of California, 18830 Road 112, Tulare 93274
⁴ Department of Veterinary Science, The Pennsylvania State University, University Park 16802

A computer module was developed and tested that used field survey and Dairy Herd Improvement Association (DHIA) data to broadly classify bacterial causes of mastitis in dairy herds. Further development of the computer model could aid interpretation of DHIA data by dairy record processing centers and herd consultants. This diagnostic module was developed with an artificial neural network, a technology that processes complex data in a manner similar to human brain function. Information describing herd management practices, quarter milk samples, and monthly DHIA data was collected from Pennsylvania dairy herds with moderate to high somatic cell counts. This information was used to develop or train an artificial neural network model that discriminated among four categories of bacterial organisms (contagious, environmental, no significant growth, and other) associated with clinical and subclinical mastitis. After training the model, new DHIA and management data were presented to the model to assess its ability to classify bacteriological etiology. When the artificial neural network was used, the probabilities of diagnosing the bacteriologic status from three randomly selected cow groups and from new untested herds ranged from 57 to 71%. Performance of the artificial neural network model was best in herds with higher frequency of minor and contagious pathogens. Prediction results for the same test data with linear discriminant analysis were less successful, ranging from 42 to 57%.

Key Words: mastitis • decision aid • artificial neural network • linear discriminant analysis

Submitted on May 3, 1999
Accepted on November 10, 1999

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