  Early detection of fish diseases and identifying the underlying causes are
crucial for farmers to take necessary steps to mitigate the potential outbreak,
and thus to avert financial losses with apparent negative implications to
national economy. Typically, fish diseases are caused by virus and bacteria;
according to biochemical studies, the presence of certain bacteria and virus
may affect the level of pH, DO, BOD, COD, TSS, TDS, EC, PO43-, NO3-N, and NH3-N
in water, resulting in the death of fishes. Besides, natural processes, e.g.,
photosynthesis, respiration, and decomposition also contribute to the
alteration of water quality that adversely affects fish health. Being motivated
by the recent successes of machine learning techniques in complex relational
data analyses in accurate classification and decision-making tasks, a
state-of-art machine learning algorithm has been adopted in this paper to
detect and predict the degradation of water quality timely and accurately, thus
it helps taking pre-emptive steps against potential fish diseases. The
experimental results show a high accuracy in detecting fish diseases particular
to specific water quality based on the algorithm with real datasets.
