A Hybrid Method For Assesment And Dignosis Of Breast Cancer.docx

A HYBRID METHOD FOR ASSESMENT AND DIGNOSIS OF BREAST CANCER ABSTRACT: Data mining is "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data." Data mining is an inter-disciplinary field, whose core is at the intersection of machine learning, statistics and databases. A major objective of this work is to evaluate data mining tools in medical and health care applications to develop a tool that can help make timely and accurate decisions. One technique used in data mining is Classification where the desired output is a set of Rules or Statements that characterize the data. Within the rule induction paradigm, the algorithm used is Hybrid Optimization. The hybrid algorithm is combination of particle swarm optimisation and Ant Colony optimisation. And also develop Classification rules to extract data from historical or training data of patients which is developed into patterns relevant for diagnosis and suitable for quicker analysis, automated processing, thus reducing cost and helping to provide enhanced care and better cure. WORK FLOW: STEP 1: Get data STEP 2: Pre-processing (Split Test and Training data) STEP 3: Apply hybrid algorithm (PSO+ACO) STEP 4: Construct the rules from the new data set STEP 5: Classification (Validate the test data)

PARTICLE SWARM OPTIMISATION Each particle keeps track of its coordinates in the problem space which are associated with the best solution (fitness) it has achieved so far. (The fitness value is also stored.) This value is called pbest. Another "best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the neighbours of the particle. This location is called lbest. When a particle takes all the population as its topological neighbours, the best value is a global best and is called gbest. The particle swarm optimization concept consists of, at each time step, changing

the

velocity

of

(accelerating)

each

particle

toward

its

pbest and lbest locations (local version of PSO). Acceleration is weighted by a random term, with separate random numbers being generated for acceleration toward pbest and lbest locations. ANT COLONY OPTIMISATION Ant colony optimization is a technique for optimization that was introduced in the early 1990’s. The inspiring source of ant colony optimization is the foraging behaviour of real ant colonies. This behaviour is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems, to continuous optimization problems, and to important problems in telecommunications, such as routing and load balancing. First, we deal with the biological inspiration of ant colony optimization algorithms.

In our proposed method we will combine both ant colony optimisation and particle swarm optimisation.