Keywords: water quality, environmental monitoring, nutritional status, data mining, back propagation, fuzzy logic, healthcare, river water, Malaysia, fuzzy inference systems, FIS, pattern extraction, health impact
Water quality in healthcare
Rivers are important freshwater resources for many countries and human beings around the world. Social well being, economics and political development have been largely related to the availability and distribution of these freshwater resources. In many parts of the world the adverse health impacts of water supply from rivers, dam construction, irrigation development and flood mitigation have led to an increased incidence of malaria, Japanese encephalitis, schistosomiasis, lymphatic filariasis and others. Hence, a study on quality of river water is the most essential part for maintaining good water quality. Vital river water quality parameters such as pH, Dissolved Oxygen (DO), biochemical oxygen demand, suspended solids, chlorides, phosphates, nitrates and sodium are important elements for the survival of the living beings in the river ecosystem. Their concentrations are used as variables of water quality. To extract information and develop knowledge patterns that exist among these parameters, a data mining approach using Fuzzy Inference System (FIS) is adopted in this study. The performance of its pattern extraction from data sets are tested using water quality data collected from river Kerayong in West Malaysia.