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Using fuzzy Ant Colony 
Optimization 
for Diagnosis of Diabetes 
Disease 
PRESENTED BY, 
NITHYA.K,DIVYA.K, 
III –CSE, 
KINGS COLLEGE OF ENGG.
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.
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.
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).
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).
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.
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.
Using fuzzy ant colony optimization for Diagnosis of Diabetes Disease
Using fuzzy ant colony optimization for Diagnosis of Diabetes Disease
Using fuzzy ant colony optimization for Diagnosis of Diabetes Disease

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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.