Book Image

Neural Network Programming with Java

By : Alan M. F. Souza, Fabio M. Soares
Book Image

Neural Network Programming with Java

By: Alan M. F. Souza, Fabio M. Soares

Overview of this book

<p>Vast quantities of data are produced every second. In this context, neural networks become a powerful technique to extract useful knowledge from large amounts of raw, seemingly unrelated data. One of the most preferred languages for neural network programming is Java as it is easier to write code using it, and most of the most popular neural network packages around already exist for Java. This makes it a versatile programming language for neural networks.</p> <p>This book gives you a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java.</p> <p>You will first learn the basics of neural networks and their process of learning. We then focus on what Perceptrons are and their features. Next, you will implement self-organizing maps using the concepts you’ve learned. Furthermore, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, and characters recognition (OCR). Finally, you will learn methods to optimize and adapt neural networks in real time.</p> <p>All the examples generated in the book are provided in the form of illustrative source code, which merges object-oriented programming (OOP) concepts and neural network features to enhance your learning experience.</p>
Table of Contents (19 chapters)
Neural Network Programming with Java
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Disease diagnosis with neural networks


For disease diagnosis, we are going to use the free dataset proben1, which is available on the web (http://www.filewatcher.com/m/proben1.tar.gz.1782734-0.html). Proben1 is a benchmark set of several datasets from different domains. We are going to use the cancer and the diabetes dataset. We added two new classes to run the experiments of each case: CancerDisease and DiabetesDisease.

Using ANN to diagnose breast cancer

Ten variables compose the breast cancer dataset, where nine are inputs and one is a binary output. The dataset has 699 records, but we excluded 16 from them, which were found to be incomplete; thus, we used 683 records to train and test a neural network.

Tip

In real practical problems, it is common to have missing or invalid data. Ideally, the classification algorithm must handle these records, but sometimes, it is recommended to exclude them since there would not be information to produce an accurate result.

The following table shows the configuration...