Analysis of artificial neural networks in the diagnosing of breast cancer using fine needle aspirates

This thesis examines how Artificial Neural Networks can be used to classify a set of samples from a fine needle aspirate dataset. The dataset is composed of various different attributes, each of which are used to come to the conclusion as to whether a sample is benign or malignant. To automate the process of analyzing the various attributes and coming to a correct prediction, a neural network was implemented. First, a Feedforward Neural Network was trained with the dataset using a Backpropagation training method and an activation sigmoid function with one hidden layer in the architecture of the network. After training, the network performed a 10-fold cross validation to determine which model had the lower error scores and would perform the best on the data. The data was looped through the model and the trained network classified the samples as either benign or malignant. Once classified, the overall accuracy, specificity and sensitivity were analyzed to measure performance. Three other neural networks were compared to the Feedforward Network to see how they performed. These three neural networks included a NEAT Neural Network, a Support Vector Machine, and a Radial Basis Function Neural Network

Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data

Accurate diagnostic detection of the cancerous cells in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Bayesian classifier and other Artificial neural network classifiers (Backpropagation, linear programming, Learning vector quantization, and K nearest neighborhood) on the Wisconsin breast cancer classification problem

Radial Basis Function Neural Networks : A Review

Radial Basis Function neural networks (RBFNNs) represent an attractive alternative to other neural network models. One reason is that they form a unifying link between function approximation, regularization, noisy interpolation, classification and density estimation. It is also the case that training RBF neural networks is faster than training multi-layer perceptron networks. RBFNN learning is usually split into an unsupervised part, where center and widths of the Gaussian basis functions are set, and a linear supervised part for weight computation. This paper reviews various learning methods for determining centers, widths, and synaptic weights of RBFNN. In addition, we will point to some applications of RBFNN in various fields. In the end, we name software that can be used for implementing RBFNNs

A breast cancer diagnosis system: a combined approach using rough sets and probabilistic neural networks

In this paper, we present a medical decision support system based on a hybrid approach utilising rough sets and a probabilistic neural network. We utilised the ability of rough sets to perform dimensionality reduction to eliminate redundant attributes from a biomedical dataset. We then utilised a probabilistic neural network to perform supervised classification. Our results indicate that rough sets was able to reduce the number of attributes in the dataset by 67% without sacrificing classification accuracy. Our classification accuracy results yielded results on the order of 93%

Ants constructing rule-based classifiers.

Classifiers; Data; Data mining; Studies;