Experimenting the dendrite cell algorithm for disease outbreak detection model

The characteristics of early outbreak signal which are weak and behaved under uncertainties has brought to the development of outbreak detection model based on dendrite cell algorithm. Although the algorithm is proven can improve detection performance, it relies on several parameters which need to be defined before mining. In this study, the most appropriate parameter setting for outbreak detection using dendrite cell algorithm is examined. The experiment includes four parameters; the number of cell cycle update, the number of dendrite cell allowed to be in population, weight, and migration threshold value. To achieve that, an anthrax disease outbreak is chosen as a case study. Two artificial anthrax datasets known as WSARE7 and WSARE58 are taken as experiment data. The experiment is measured based on five metrics; detection rate, specificity, false alarm rate, accuracy, and time taken to produce result. Besides that, a comparison is made with Cumulative Sum, Exponentially-weighted Moving Average, and Multi Layer Perceptron. From the experiment, the best parameter setting for anthrax outbreak using dendrite cell algorithm is identified whereby it proven can helps the model to produce a good detection result between detection rate and false alarm rate. Since each outbreak disease carries different outbreak characteristic, the parameter setting for different outbreak might be different.