A Fuzzy-Neural-Genetic Algorithm Approach for Gene Expression and Micro Array Analysis for Breast Cancer Identification

A Fuzzy-Neural-Genetic Algorithm Approach for Gene Expression and Micro Array Analysis for Breast Cancer Identification

Velpula Nagi Reddy A. Rama Swamy Reddy

Research Scholar, Department of IT, VFSTR Deemed to be University, Vadlamudi, Guntur, India

Professor in CSE, VFSTR Deemed to be University, Vadlamudi, Guntur, India

Corresponding Author Email: 
nagireddy547@gmail.com, ramaswamyreddymail@gmail.com
26 December 2017
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3 January 2018
| | Citation



The machine assisted medicinal finding of intricate enclosed works, for example, Breast Cancer (BC) is measured as the most adamant hazard and the main source of death among ladies all in all. The superior part of the precedent work concerning this matter was on the study of disease transmission, learning of (BC) and routine with regards to breast self-examination (BSE), etiological variables, and rate of survival. Earlier detection of Breast Cancer saves enormous lives, crashing and burning which may provoke to other extraordinary issues expediting sudden deadly end. Its cure rate and desire depend predominantly on the early recognizable proof and finding of the contamination. A champion among the most surely understood sorts of remedial demonstrations of disregard universally is a screw up in determination. Today, there is immense use of information mining methods and process like Knowledge disclosure improvement for expectation. Critical taking in can be found from use of data mining techniques in social protection system. Untimely prediction and identification of Breast Cancer (BC) is a vital, certifiable therapeutic issue. In this paper, we suggest a genetic algorithm approach for the order of small scale cluster information utilizing quality expression to assemble a Breast Cancer (BC) analysis framework with high capacities and to predict the tumor accurately. A few investigations were directed utilizing these calculations. The accomplished forecast exhibitions are practically identical to existing systems. In any case, we discovered that proposed algorithm has a vastly improved execution than the different systems. The predictive investigation strategies of information mining assemble an information forecast display by examining the present history of patients in order to break down what's to come information. This paper audits the consequences of different arrangement systems that have been shown in the look into articles from quite a long while and makes a correlation between the displayed yields of the before works. In addition, experimentation is additionally directed to check the realness of the best clustering algorithm as a verification of idea. We concentrate on joining fuzzy-neural thoughts and Genetic algorithms to obviously create indicative structures to help and to comprehend and assess its outcomes with high characterization execution with accuracy. Our outcomes demonstrate that the proposed fuzzy-neural genetic qualities approach produces frameworks that accomplish high grouping execution, with basic and well interpretive standards and a decent level of certainty giving best results when compared with the available genetic algorithms.


Breast cancer, Analysis troubles, Fuzzy-neural systems, Genetic algorithms, Machine assisted diagnosis

1. Introduction
2. Literature Survey
3. Fuzzy-Neural Systems Mechanisms
4. Proposed Genetic Algorithm
5. Results
6. Conclusion

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