A New Method for Breast Cancer Diagnosis Using Neural Network and Genetic Algorithms

A new diagnosis system using the combination of neural networks and genetic techniques for breast cancer with classification accuracy of about %98.5% can be used for early detection of breast cancer without needing to undergo clinical trial.

Abstract

Breast cancer in women is one of the fatal diseases that takes the lives of thousands of women around the world every year while if its common signs and symptoms are identified in time and at the early stages the patients can be easily treated. Unfortunately, the symptoms are usually detected when the cancer has developed and spread in the body and it is too late for treatment and. In addition to genetic factors, other factors such as age and weight play an important role in developing this disease. Breast cancer has hidden patterns which specialists and researchers usually fail to discover without data mining techniques. Breast cancer patients’ files can contain valuable information which can be discovered through data mining techniques. One of the characteristics of data mining is the effective searching and computing large sets of data in the medical domain. The aim of this study is therefore to develop a new diagnosis system using the combination of neural networks and genetic techniques. To evaluate the proposed method, we performed several experiments on a breast cancer dataset which is available in UC Irvine machine learning repository. The experimental results show that the method can be used to obtain efficient automatic diagnostic systems for breast cancer with classification accuracy of about %98. The proposed diagnosis system can be used for early detection of breast cancer without needing to undergo clinical trial.