  The aim of this study was to build a modelling framework that would allow us
to be able to detect mastitis infections before they would normally be found by
farmers through the introduction of machine learning techniques. In the making
of this we created two different modelling framework's, one that works on the
premise of detecting Sub Clinical mastitis infections at one Somatic Cell Count
recording in advance called SMA and the other tries to detect both Sub Clinical
mastitis infections aswell as Clinical mastitis infections at any time the cow
is milked called AMA. We also introduce the idea of two different feature sets
for our study, these represent different characteristics that should be taken
into account when detecting infections, these were the idea of a cow differing
to a farm mean and also trends in the lactation. We reported that the results
for SMA are better than those created by AMA for Sub Clinical infections yet it
has the significant disadvantage of only being able to classify Sub Clinical
infections due to how we recorded Sub Clinical infections as being any time a
Somatic Cell Count measurement went above a certain threshold where as CM could
appear at any stage of lactation. Thus in some cases the lower accuracy values
for AMA might in fact be more beneficial to farmers.
