Skin Disease analysis using Intuitionistic Fuzzy Set
A. Manimaran1, B. Praba2, V. M. Chandrasekaran1*, Karan Agrawal3, Akanksha Miharia3
1 School of Advanced Sciences, VIT University, Vellore-6320141
2SSN College of Engineering, Kalavakkam, Chennai – 603110
3School of Computing Sciences and Engineering, VIT University, Vellore-6320143
*Corresponding Author E-mail: marans2011@gmail.com, prabab@ssn.edu.in, vmcsn@yahoo.com, karan.agrawal170@gmail.com, akanksha_miharia@outlook.com
ABSTRACT:
Skin diseases occurs in different patterns due to wide ranges of factor such as climatic conditions, geographical locations, pollution, hygienic standards, age, level of health care and many more. These diseases cause a lot of distress in human health. Intuitionistic fuzzy and intuitionistic fuzzy soft set theory plays a major role in handling vague concepts and it has been applied in many fields. In this paper we aim at finding the age group in which maximum skin diseases occurs using the concept of intuitionistic fuzzy soft set by using AGEMSD measure.
KEYWORDS: Intuitionistic fuzzy, membership value, non membership value, intuitionistic fuzzy soft decision set, rank value, AGEMSD.
1. INTRODUCTION:
Skin diseases are considered to be one of the most commonly occurring condition’s for attending primary health care amongst patient population. There are several types of skin diseases. The types of skin disease that we have considered in our study are Acne Vulgaris, Atomic dermatitis, chronic ulcer, squamous cell carcinoma, Melanoma, Corns and Disease of hair.
The theory of fuzzy sets introduced by Zadeh [1] and he has showed various applications in many fields. It is very useful as it handles uncertainty and vagueness and helps us derive a valid conclusion.
Intuitionistic fuzzy set was introduced by K.T. Atanassov [2] as an extension of the standard fuzzy set. To make it more accurate intuitionistic fuzzy was introduces whose concept has been used by researchers P. A. Ejegwa [3]. After Zadeh, many researchers Muthumeenakshi [4] and Manimaran [5] have used the fuzzy set for decision making. In this paper we concentrate on intuitionistic fuzzy soft set. We will be using the concept of intuitionistic fuzzy soft theory for finding the age group in which maximum skin diseases occur as it handles uncertainty and vagueness and gives us appropriate result.
2. PRELIMINARIES:
Acne Vulgaris:
It is a long term skin disease that occurs when hair follicles become clogged with dead skin cells and oil from the skin.
Chronic ulcer:
It is a sore on the skin or a mucous membrane, accompanied by the disintegration of tissue.
Squamous cell carcinoma:
It is a cancer of a kind of epithelial cell, the squamous cell.
Melanoma:
It is a type of cancer that develops from the pigment-containing cells known as melanocytes.
Corns:
It is a distinctively shaped callus of dead skin that usually occurs on thin or smooth skin surfaces, especially on the dorsal surface of toes or fingers
Disease of hair:
These are diseases that result in the poor condition of scalp and hair and may also cause hair loss.
Definition 2.1: [1, 3]
Let
be
a nonempty set. A fuzzy set
drawn
from
is
defined as
where
is
the membership function of the fuzzy set
.
Example 2.1: [3]
Consider the temperature of a patient in degrees
Celsius. Let
.
The fuzzy set A “High temperature” may be defined as,
where the numbers 0, 0.1, 0.5, 0.8, and 1 express
the degree to which the corresponding temperature is high.
Definition 2.2:[3]
Let
be
a nonempty set. An intuitionistic fuzzy set (IFS)
in
is
an object having the form
where
the functions
define
respectively, the degree of membership and degree of non membership of the element
to
the set
,
which is a subset of
,
and for every element
Furthermore,
we have
called
the intuitionistic fuzzy set index or hesitation margin of
.
is
the degree of indeterminacy of
to
the IFS
and
and
.
expresses
the lack of knowledge of whether
belongs
to IFS
or
not.
Definition 2.3: [3]
Let
be
IFS, then;
1.
is called the degree of indetermacy
of the element
.
2.
is
called the degree of favour of
.
3.
is
called the degree of against of
.
Example 2.2:
For example, let 𝐴 be an intuitionistic fuzzy set with
then,
.
It can be interpreted as “the degree that the object 𝑥 belongs to IFS 𝐴 is 0.5, the degree that the object
𝑥 does not belong to IFS 𝐴 is 0.3, the degree of hesitancy
or indetermacy of 𝑥 belonging to IFS 𝐴 is 0.2, the degree of favour of 𝑥 belonging to IFS 𝐴 is 0.6 and the degree of against of 𝑥 not belonging to IFS 𝐴𝑥 is 0.36”.
Definition 2.4:[6]
A pair
is
called a soft set (over U) if and only if F is a mapping of E
into the set of all subsets of
the set 𝑈. In other words, the soft set is a parameterized
family of subsets of the set 𝑈. Every set
,
from this family may be considered as the set of e -approximate elements
of the soft set.
Example 2.3:[7]
Let be a universal set and be a set of parameters.
If and then the soft set
is
written by.
Definition 2.5: [4]
Let U refers to an initial inverse, E is the set of all parameters, is the power set of U and X
be the fuzzy set over E. An FS-set on U is defined by the pair
where the function is called approximate function such that if and is called membership function of FS-set. The value of is the degree of importance of the parameter x, and depends on the decision makers requirements. The set of all FS-sets over U will be denoted byFPS(U).
Definition 2.6:[4]
Let Then a fuzzy soft decision (FSD) set of denoted by and is defined
by which is a fuzzy set over U , its membership function is defined by and
Definition 2.7: [8]
Let U be an initial inverse set and E be the set of parameters. Let denotes the collection of all intuitionistic
by
Example 2.3: [8]
Consider the following example:
Let describes the character of the students with respect to the given parameters,
for finding the best student of an academic year. Let the set of students under
consideration is
.
Let and.
Then the family of
is
an intuitionistic fuzzy soft set.
Definition2.8:
Let U refers to and initial inverse, E is a set of parameters,
is the power set of U, and X be an intuitionistic fuzzy soft set
over E. An IFS-set on U is defined by where the function is called approximate
function such that , is called membership function of IFS-set
and
is
called the non membership function of IFS-set
.
The value of
and
are
the degree of importance of the parameter x, and depends on the decision
makers requirements. The set of all IFS-sets over U will be denoted byIFPS(U).
3. RESEARCH OBJECTIVE:
To find the age group at which maximum skin diseases occur using intuitionistic fuzzy set.
4. METHODOLOGY:
Definition 4.1: (Intuitionistic Fuzzy Soft Decision Set)
Let
then
an intuitionistic fuzzy soft decision is denoted by and is defined as which is an intuitionistic fuzzy
set over U, its membership function is defined by and nonmembership function
is defined by and
4.1. Analysis using IFSD Set
Step 1:
To apply Intuitionistic Fuzzy Soft Decision Set in finding the age-group that suffers maximum skin diseases the age-groups are taken as the universal set and the skin diseases are taken as the set of Constraints as, where
· M1= 00-04 years
· M2= 05-14 years
· M3= 15-24 years
· M4= 25-44 years
· M5= 45-64 years
· M6= 65-74 years
· X1=Acne Vulgaris
· X2=Atomic dermatitis
· X3=Chronic ulcer
· X4=Squamous cell carcinoma
· X5=Melanoma
· X6=Corns
· X7= Disease of hair
The Soft set
is
designed as
Step 2:
The constraints are ranked according to the ranks given by the 501 respondents. The sums of ranks with respective constraints are given in the following Table.
Ranks reported by the respondents are sum up and values are assigned in the descending order. Maximum value (7) is given to the least number and the remaining values are given with respect to the sum of ranks. Then the fuzzy membership values have been assigned by dividing the rank value (RV) with the number of respondents as follows.
The FS-set
is
designed as
Step 3:
The FSD-set
has been attained using the definition 5
Step 4:
It is clear that i.e., Age-group 65-74 years has the maximum value among the six selected measures.
5. CONCLUSION:
Hence we conclude that age-group 65-74 faces maximum skin diseases using intuitionistic fuzzy soft decision set.
6. REFERENCES:
1. L. A. Zadeh, Fuzzy Sets, Information and Control, 8(1965), 338–353.
2. K.T. Atanassov, Intuitionistic Fuzzy Sets, Fuzzy Sets and Systems, 20(1986), 87–96.
3. P. A. Ejegwa, S.O. Akowe, P.M. Otene, J.M. Ikyule, An Overview On Intuitionistic Fuzzy Sets, International Journal of Scientific & Technology Research, 3(3) (2014), 142-145.
4. M. Muthumeenakshi and P. Muralikrishna, A Study on SFPM Analysis Using Fuzzy Soft Set, International Journal of Pure and Applied Mathematics,94(2) (2014), 207 - 213.
5. A. Manimaran, V.M. Chandrasekaran, B. Praba, A Review of Fuzzy Environmental Study in Medical Diagnosis System, Research journal of Pharmacy and Technology, 9 (2) (2016), 177 – 184.
6. Manoj Borah, Tridiv Jyoti Neog, Dusmanta Kumar Sut, A Study on Some Operations of Fuzzy Soft Sets, International Journal of Modern Engineering Research, 2(2) (2012), 219-225.
7. P. K. Maji, R. Biswas and A.R. Roy, Fuzzy Soft Sets, Journal of Fuzzy Mathematics, 9(3) (2001), 589-602.
8. Bivas Dinda and T.K. Samanta, Relations on Intuitionistic Fuzzy Soft Sets, General Mathematical Notes, 1(2) (2010), 74-83.
Received on 04.06.2017 Modified on 14.07.2017
Accepted on 20.08.2017 © RJPT All right reserved
Research J. Pharm. and Tech. 2018; 11(1): 79-82.
DOI: 10.5958/0974-360X.2018.00015.X