Artificial Neural Network plays an important role in medical diagnostics field and used by medical practitioners and domain specialists for diagnosis and treatment with ultimate accuracy. In this paper, a medical diagnosis system is proposed for predicting Generalized Anxiety Disorder (GAD). In today’s world of computational Intelligence, Swarm Intelligence technique is one of the successive ways to solve hard medical problems. Particle Swarm Optimization (PSO) imitates the behavior of a swarm of insects or a group of fish or birds. In this paper the relative advantages of genetic algorithm, Particle Swarm Optimization and Artificial Neural Network (ANN) are combined to achieve the desired accuracy. ANN’s are often used as a powerful discriminating classifier for tasks in medical diagnosis for early detection of diseases. The data set on this study is composed of 200 patients with various sign symptoms. The objective of this paper is to determine the weights of the neural network using genetic algorithm in less number of iterations and PSO algorithm for feature Reduction. For training the network Quasi-newtonalgorithm is used in this study using various training algorithm parameters. The accuracy obtained using this approach is 98.56%.

Artificial Neural Network (ANN); Genetic Algorithm (GA); Particle Swarm Optimization (PSO); Generalized Anxiety Disorder (GAD)

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