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PREDICTIVE ANALYSIS OF
HEALTH RECORDS
Umang Shukla
Pranay Sharma
Krishnan Iyer
Monica

? About dataset
? What is fuzzy logic
? Basics of fuzzy
? Work done
? Observations
OVERVIEW

ABOUT THE DATASET
Pima Indians Diabetes Database
Sources : UCI Machine Learning Repository
Owners : National Institute of Diabetes and Digestive and Kidney Diseases
Donor : Vincent Sigillito
Date Received : 9th May 1990
Patients are females of age greater than 21 years of Pima Indian Heritage.
Number of Instances: 768
Number of Attributes: 8
These attributes are :
1. Number of times pregnant
2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test
3. Diastolic blood pressure (mm Hg)
4. Triceps skin fold thickness (mm)
5. 2-Hour serum insulin (mu U/ml)
6. Body mass index (weight in kg/(height in m)^2)
7. Diabetes pedigree function
8. Age (years)
9. Class variable (0 or 1)

What is Fuzzy Logic ?
Fuzzy logic is an approach to computing based on "degrees of truth"
rather than the usual "true or false" boolean logic
It was first advanced by Dr. Lotfi Zadeh of the University of California
He said any logical system could be fuzzified.

LET��S REVIEW SOME CONCEPTS
Fuzzy Sets
Let X be a non empty set, A fuzzy set A
in X is characterized by its membership
function ?A: X -> [0,1], where ?A(x) is
the degree of membership of element x
in fuzzy set A for each x �� X .
Operations
Union
Intersection
Complement
Comtainment
Membership Function
They map elements of a fuzzy set to
real numbered values in the interval 0
to 1.
Example:- Triangular, Trapezoidal, S-
shaped, Sigmoid, Pi-function
Fuzzification
The process of
transforming crisp
(bivalued) input values into
linguistic values is called
fuzzification
Defuzzification
Defuzzification converts the fuzzy
values into crisp (bivalued) value.
Types :- Max-membership method
Centroid method
Weighted average method

5.
FUZZIFICATION OF
BINNED INPUT
AND OUTPUT

EVALUATION
TECHNIQUES
INTERACTIVE

This can be done by evalfis function on matlab
output= evalfis(input,fismat)
Evalfis() has the following arguments:
? input: a number or a matrix specifying input values.
? fismat: an FIS structure to be evaluated.
ON MATLAB TERMINAL
EVALUATION
TECHNIQUES

Before understanding the results we need to know
about the trapezoidal shaped member function which
we used to define input variable.
tramf = f(x,a,b,c,d)
OBSERVATIONS

In our test dataset we had 332 instances.
We evaluated our FIS model for 5%, 10%, 15% and 20%
variance of the a, b, c, d point for each input member.
Next we took 0.65 as our membership value for output variable
to classify predictions as ��yes�� or ��no��.
However, It was observed that none of these changes in input
variable boundary affected the accuracy of the predictions with
exception to changes done in the member ��plasma��.
On digging deeper we found out the reason for such a
behaviour, we observed that even though accuracy was not
changing these variance indeed affect the membership value of
output but none of were big enough to cross the 6.5 barrier
which we had set for output classification.
OBSERVATIONS

OBSERVATIONS
MODEL ACCURACY
J48 74.14%
DEFAULT(all with 5% variance) 80.722%
10% variance in plasma 81.024%

OBSERVATIONS
We observed that fuzzy system performs better than our J48 for
same classification model as J48 uses crisp data values.
As only plasma was affecting the accuracy we found that it was
so because of plasma was involved in all the rules defined
above.
As we increased the input variance of plasma the accuracy
showed an increase but only upto a particular level.

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Predictive Analysis of Health Records using MATLAB

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Predictive Analysis of Health Records using MATLAB