Dengue fever is considered as one of the most common mosquito borne diseases worldwide. Dengue outbreak detection can be very useful in terms of practical efforts to overcome the rapid spread of the disease by providing the knowledge to predict the next outbreak occurrence. Many studies have been conducted to model and predict dengue outbreak using different data mining techniques. This research aimed to identify the best features that lead to better predictive accuracy of dengue outbreaks using three different feature selection algorithms; particle swarm optimization (PSO), genetic algorithm (GA) and rank search (RS). Based on the selected features, three predictive modeling techniques (J48, DTNB and Naive Bayes) were applied for dengue outbreak detection. The dataset used in this research was obtained from the Public Health Department, Seremban, Negeri Sembilan, Malaysia. The experimental results showed that the predictive accuracy was improved by applying feature selection process before the predictive modeling process. The study also showed the set of features to represent dengue outbreak detection for Malaysian health agencies.
Near-real-ime assay is anassay method that the whole process from sampling until results could be obtained in approximately Iess than one hour. The ElIman assay for acetyl cholinesterase (AChE) has near real-time potential due to its simplicity and fast assay time. The commercial acetylcholinesterase from Electrophorus electricus is well known for its uses in insecticides detection. A lesser known fact is AChE is also sensitive to heavy metals. A near real-time inhibitive assay for heavy metals using AChE from this source showed promising results. Several heavy metals such as copper, silver and mercury could be etected with IC50 values of1.212, 0.1185 and 0.097 mg I-1, respectively. The Limits of Detection (LOD) for copper, silver and mercury were 0.01, 0.015 and 0.01 mg I-1, respectively. TheLimits of quantitation (LOQ) or copper, silver and mercury were 0.196, 0.112 and 0.025 mg I-1, respectively. The LOQvalues for copper, silver and mercury were well below the maximum permissible limit for these metal ions as outlined by Malaysian Department of Environment. A polluted location demonstrated near real-time applicability of the assay with variation oftemporal levels of heavy metals detected. The results show that AChE from Electrophorus electricus has the potential to be used as a near real-time biomonitoring tool for heavy