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.