  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 the
national economy. Typically, fish diseases are caused by viruses and bacteria;
according to biochemical studies, the presence of certain bacteria and viruses
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, 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 to take
preemptive steps against potential fish diseases. The experimental results show
high accuracy in detecting fish diseases specific to water quality based on the
algorithm with real datasets.
