5.2 Proposed Diagnostic Model
This section describes the proposed diagnostic model for the early detection and prediction of dengue disease. A PSO-ANN based classifier is applied to accurate detection of dengue disease. In the proposed classifier, PSO is applied to optimize the weight and bias parameters of ANN technique. In the proposed model, 10-cross fold validation technique is implemented which divides the dengue data into ten equal parts. The detailed description of this technique is given in subsection. In the proposed model, sixteen input neurons are defined in input layer corresponding to sixteen attributes of dengue dataset. The hidden layer consists of seventeen neurons, whereas output layer consist of only two neurons corresponding of class labels such as dengue positive and dengue negative. The weight of each neuron is computed using PSO technique and the optimized weight is used to train the neural network. The classification accuracy and confusion matrix are considered as a performance parameter to evaluate the performance of the proposed diagnostic model. Fig. 4 shows the proposed diagnostic model for earlier detection of dengue disease.