Improved Genetic-Fuzzy System For Breast Cancer Diagnosis

PubMed, 2016

Computeraided diagnosis of breast cancer is an important medical approach. In this research paper, we focus on combining two major methodologies, namely fuzzy base systems and the evolutionary genetic algorithms and on applying them to the Saudi Arabian breast cancer diagnosis database, to aid physicians in obtaining an earlycomputerized diagnosis and hence prevent the development of cancer through identification and removal or treatment of premalignant abnormalities; early detection can also improve survival and decrease mortality by detecting cancer at an early stage when treatment is more effective. Our hybrid algorithm, the geneticfuzzy algorithm, has produced optimized systems that attain high classification performance, with simple and readily interpreted rules and with a good degree of confidence.

International Journal of Fuzzy Logic Systems, 2020

Breast cancer is conspicuously one of the most common diseases that cause death for women. Besides, it is increasing with high rates. Consequently, Breast Cancer must be discovered in early stages to avoid death or losing part of the body due to late diagnosis. Thus, there are many researches for computerizing breast cancer diagnosis with different techniques. It reduces human decision rate in order to decrease the mortality rate through the disease. Therefore, we have a major motivation for this highly significant work. The primary focus of the research is to produce a multi-model that can predict the diagnosis whether benign (noncancerous) or malignant (cancerous) nature of a tumor with high accuracy using two methods. The first method is a combining of two major methodologies, namely the fuzzy based systems and the evolutionary genetic algorithms (GFIS). The second method intends to an integrated view of implementing an adaptive neuro-fuzzy inference system (ANFIS) with feature selection using principle component analysis (PCA). Wisconsin breast cancer database (WBCD) is applied because it contained records of patients with known diagnosis. The proposed target of this research compares breast cancer diagnosis based on physical characteristics of the tumour between GFIS and ANFIS. GFIS has achieved a high performance with 97.7% however ANFIF has achieved the highest accuracy with 99.1%.

2008

The automatic diagnosis of breast cancer is an important, real-world medical problem. In this paper we give an introduction to fuzzy systems, genetic algorithms and artificial immune system, and then we introduce a hybrid algorithm that gathers the genetic algorithms with the artificial immune system in one algorithm. The genetic algorithm, the artificial immune system and the hybrid algorithm were implemented and tested on the Wisconsin breast cancer diagnosis (WBCD) problem in order to generate a fuzzy rule system for breast cancer diagnosis. The hybrid algorithm generated a fuzzy system which reached the maximum classification ratio earlier than the two other ones. The motivations of using fuzzy rules incorporate with evolutionary algorithms in the underline problem are attaining high classification performance with the possibility of attributing a confidence measure (degree of benignity or malignancy) to the output diagnosis beside the simplicity of the diagnosis system which means that the system is human interpretable.

Filomat, 2016

Breast Cancer (BC) is considered as the most implacable malignancy and the leading cause of mortality among women in general and in Saudi Arabia specially. Most of the previous work in Saudi Arabia on this subject was on epidemiology, knowledge of (BC) and practice of breast self-examination (BSE), etiological factors, metastases and rate of survival. Early detection and diagnosis of Breast Cancer (BC) is an important, real-world medical problem. In this paper, we propose a soft computing methodology to build a Breast Cancer (BC) diagnosis system with high capabilities as described by Andres et al. [4] but on the Saudi Arabian breast cancer dataset and using a simplified fitness function. We focus on combining fuzzy concepts and genetic algorithms so as to automatically produce diagnostic systems to support and assist the expert to understand and evaluate its results with high classification performance.

1998

Abstract| We present an evolutionary approach for discovering fuzzy systems for breast cancer diagnosis. By judiciously designing an appropriate representation scheme (genome) and tness function, the genetic algorithm is then able to produce successful systems. These surpass the best known systems to date in terms of combined performance and simplicity.