Cholera forecast based on mining association rules

Recently, the epidemic forecast has been interest of many researchers. Traditionally, mathematical epidemiological models have been used for epidemic forecast. However, these models require some functional dependency assumptions that may not be always practical. Other approaches based on data mining have achieved promising results thanks to the development of efficient data mining techniques and computing technologies. This paper proposed a method for cholera forecast based on association rule mining using the non-standard distribution dataset. Experimental results show that the proposed method is suitable for cholera forecast and can be used as an important input in the decision making process of the preventive healthcare.