Title: Breast cancer diagnosis using a Minkowski distance method based on mutual information and genetic algorithm
Authors: Neha Vutakuri; Amineni Uma Maheswari
Addresses: McLean High School, Virginia, USA ' Department of Bioinformatics, SVIMS University, Tirupati, AP, India
Abstract: Breast cancer is one of the most frequently diagnosed cancers and can lead to death in women worldwide. Diagnosing breast cancer is one of the most challenging tasks as symptoms may only be present in later stages. This paper presents a mutual information genetic algorithm (MIGA) where MIGA is a combination of two algorithms, mutual information (MI) and genetic algorithm (GA). The method of this work is as follows: attributes of breast cancer patients were collected using the Breast Cancer Wisconsin Diagnostic dataset. A breast cancer diagnosis system was developed using a GA and hybrid algorithm (genetic and K-nearest neighbour) and then used MI and GA. GA fitness was calculated using the Minkowski distance method. The obtained solutions are verified for three algorithms (GA, GA+KNN, and MIGA). Finally, the proposed MIGA algorithm results show that the highest accuracy (99%) obtained with the GA-based MI features.
Keywords: breast cancer diagnosis; genetic algorithm; mutual information; breast cancer Wisconsin dataset; Minkowski distance method.
International Journal of Advanced Intelligence Paradigms, 2020 Vol.16 No.3/4, pp.414 - 433
Received: 04 Jul 2017
Accepted: 22 Oct 2017
Published online: 01 Jun 2020 *