ABSTRACT
Medical diagnosis is a vital step that allows for a broad understanding of diseases. Though there are numerous machines to detect a patient's ailments, however, timely and correct diagnosis of the disease is the need of the hour. Driven by large amount of data, computers are being used to perform these complex tasks in less time. Machine learning is a multi-disciplinary technology that has spread its wings across various industries, saving economy and time. With the emergence of neural networks, right decision about a patient's health can be made. Furthermore, neural networks helps doctors plan surgical procedures and also aids them in precise detection of the disease based on real-time data. This chapter presents the neural networks-based models that are used in medical diagnosis. It covers the learning methods of neural networks with their applications in health care. It describes how supervised and unsupervised learning algorithms are used for disease prediction. The chapter also demonstrates how reinforcement learning is applied to proper clinical diagnosis. Several neural networks, such as Hopfield neural networks, perceptron, conventional neural network (CNN), recurrent neural network (RNN), radial basis function network, are also discussed.
