Investigating the Applicability of Logistic Regression and Artificial Neural Networks in Predicting Breast Cancer

Abstract

Breast cancer has become recently the most common cancer and a major cause of death among women all over the world and especially in developing countries like Iraq. This study  aims to predict the type of breast tumor whether benign or malignant through the different models that were built using logistic regression and neural networks which is expected to be helpful for oncologists in diagnosing the type of breast tumor. Four models were set using binary logistic regression  and two different types of artificial neural networks namely multilayer perceptron (MLP) and radial basis function (RBF). Both validated and trained models were evaluated using  different performance metrics like accuracy or correct classification rate (CCR), receiver operating characteristic (ROC) curves, area under ROC curve (AUC), sensitivity and specificity. Dataset has been downloaded from the machine learning repository of University of California, Irvin (UCI ml repository) that consists of 9 attributes  and 699 valid instances.    Firstly, some preprocessing was done to cleanse the data, then the models were built using the Logistic Regression method and Artificial Neural Networks and a comparison was done to find out which model will give the highest performance. Each model was validated with a different dataset than that used for developing the models. The analysis of the results showed that the Radial Basis Function neural network model is the best classifier in the prediction of the type of breast tumors since it had recorded the highest performance in terms of correct classification rate (accuracy), sensitivity, specificity, and AUC among all other models.