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# Predictive Analysis of Health Records using MATLAB

Predictive Analysis of Health Records using MATLAB's fuzzy toolbox.
The dataset is Pima Indians Diabetes Database from UCI Machine learning repository

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### Predictive Analysis of Health Records using MATLAB

1. 1. PREDICTIVE ANALYSIS OF HEALTH RECORDS Umang Shukla Pranay Sharma Krishnan Iyer Monica
2. 2. ➜ About dataset ➜ What is fuzzy logic ➜ Basics of fuzzy ➜ Work done ➜ Observations OVERVIEW
3. 3. 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)
4. 4. 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.
5. 5. 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 µA: X -> [0,1], where µA(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
6. 6. WORK DONE
7. 7. 1. DATA CLEANING
8. 8. 2. SPLITTING OF DATASET
9. 9. 3. BINNING OF TRAINING SET
10. 10. 4. RULE GENERATION USING J48
11. 11. Classification tree by J48
12. 12. 5. FUZZIFICATION OF BINNED INPUT AND OUTPUT
13. 13. Fuzzy Inference System
14. 14. 6. DEFINING FUZZY RULE
15. 15. Fuzzified Rules
16. 16. 7. TEST SET EVALUATION
17. 17. EVALUATION TECHNIQUES INTERACTIVE
18. 18. 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
19. 19. RESULTS AND OBSERVATION
20. 20. 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
21. 21. 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
22. 22. OBSERVATIONS MODEL ACCURACY J48 74.14% DEFAULT(all with 5% variance) 80.722% 10% variance in plasma 81.024% 15% variance in plasma 81.626% 20% variance in plasma 81.626%
23. 23. 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.
24. 24. Thanks! Any questions?
• #### surapanenij

Nov. 26, 2019
• #### eseeesee

Jun. 21, 2016

Predictive Analysis of Health Records using MATLAB's fuzzy toolbox. The dataset is Pima Indians Diabetes Database from UCI Machine learning repository

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