![REFERENCES
[1].http://www.diabetes.org/diabetes-basics (last accessed:
November 2009)
[2].Marco Dorigo, Christian Blum, Ant colony optimization
theory: A survey, Theoretical Computer Science Vol.344,
pp. 243 - 278, 2005.
[3].Urszula Boryczka, Finding groups in data: Cluster
analysis with ants, Applied Soft Computing, Vol. 9, pp.61-
70, 2009.](https://image.slidesharecdn.com/usingfuzzyantcolonyoptimization-141106003739-conversion-gate01/85/Using-fuzzy-ant-colony-optimization-for-Diagnosis-of-Diabetes-Disease-6-320.jpg)
Using fuzzy ant colony optimization for Diagnosis of Diabetes Disease
- 1. Using fuzzy Ant Colony Optimization for Diagnosis of Diabetes Disease PRESENTED BY, NITHYA.K,DIVYA.K, III –CSE, KINGS COLLEGE OF ENGG.
- 2. OBJECTIVES The Objective of this paper is to utilize ACO to extract a set of rules for diagnosis of diabetes disease. Since the new presented algorithm uses ACO to extract fuzzy If-Then rules for diagnosis of diabetes disease, we call it FADD. We have evaluated our new classification system via Pima Indian Diabetes data set. Results show FADD can detect the diabetes disease with an acceptable accuracy.
- 3. INDRODUCTION Diabetes is one of the most dangerous diseases, named Silent killer. Diabetes increases the risk of blindness, blood pressure, heart disease, kidney disease Ant colony optimization (ACO) has been successfully used for the classification task. The proposed method has been tested using the public Pima Indian Diabetes data set.
- 4. ANT COLONY OPTIMIZATION Ant algorithms are based on the cooperative behavior of real ant colonies. the ACO metaheuristic was proposed as a common framework for existing applications. which is based on a simple form of indirect communication through the pheromone, called stigmergy Each ant builds a possible solution to the problem by moving through a finite sequence of neighbor states (nodes).
- 5. THE PROPOSED METHOD ACO algorithm has recently been used in various kinds of data mining problems such as clustering, and classification A.A GENERAL DESCRIPTION Step1: Set the Discovered Rules as empty Step2: for each class Step2-1: Call FADD(fig.2.) for learning the rules of each class. Step2-2: Add the rules that recently learned (by step 2-1) Step2-3: Remove the covered samples of Training Set. Step 3: Compute the grade of certainty CF for each rule of the Discovered Rules. Step4: For each input pattern Xp=(x1, x2, x3, ..., xn).
- 6. REFERENCES [1].http://www.diabetes.org/diabetes-basics (last accessed: November 2009) [2].Marco Dorigo, Christian Blum, Ant colony optimization theory: A survey, Theoretical Computer Science Vol.344, pp. 243 - 278, 2005. [3].Urszula Boryczka, Finding groups in data: Cluster analysis with ants, Applied Soft Computing, Vol. 9, pp.61- 70, 2009.
- 7. CONCLUSION The main new features of the presented algorithm are as follows: 1. Introducing a new framework for learning the rules 2.A different strategy for controlling the influence of pheromone values was studied. 3.There are two important concepts in ACO that are: Competition and Cooperation.