Developing a Fuzzy Database System for Heart Disease Diagnosis

Transcription

1 Developing a Fuzzy Database System for Heart Disease Diagnosis College of Information Technology Jenan Moosa Hasan

2 Databases are Everywhere!

3 Linguistic Terms

4 V a g u e Hazy Nebulous Unclear Enigmatic Uncertain Ambiguous N u l l Obscure i n f o r m a t i o n IMPRECISE I N C O M P L E T E Not Sure

5 FUZZY D A T A B A S E

6 The theory of fuzzy sets is a step toward a rapprochement between the precision of classical mathematics and the pervasive imprecision of the real world. Lotfi A. Zadeh

7 Problem Statement Prevention is better than cure Medical diagnosis is an art. There are many factors and indications that should be considered, and finding the relationships among diseases is a complicated task that sometimes requires a sixth sense. It can affect human s life! Heart Disease Diagnosis

8 Heart Disease

9 Objectives Develop a fuzzy database system for heart disease diagnosis Review and compare different approaches and techniques used. Discover the appropriate input values that give the most accurate prediction result. Study the different defuzzification methods and compare them in order to specify the preferable solution.

10 What is Fuzzy Logic? Non-Fuzzy FUZZY x { 0, 1 } x [ 0, 1 ] Fuzzy Set

11 Membership Functions The membership functions define how each point in the input space (discourse) is mapped to a value between 0 and 1. Gaussain Singleton Triangular Trapezoid

12 FIS (Fuzzy Inference System) (1/3) Fuzzification: Crisp Inputs Membership Functions Fuzzification Fuzzy Inputs

13 FIS (Fuzzy Inference System) (2/3) Rule Based Fuzzy Inputs Rule-Based FIS Fuzzy Output

14 FIS (Fuzzy Inference System) (3/3) Defuzzification: Fuzzy Output Defuzzification Method Defuzzification Crisp Output

15 Defuzzification Methods (Naaz, et al., 2011)

16 Related Work Work Inputs System Method (Adeli & Neshat, 2010) 13 Fuzzy System Mamdani, Centroid (Kumar & Kaur, 2013) 5 Fuzzy System Mamdani, Centroid (Kumar, 2013) 13 Fuzzy System ANFIS (Pamela, et al., 2013) 12 Fuzzy System Mamdani, Centroid (Chitra, et al., 2013) 13 Fuzzy System Fuzzy C-means Clustering (Parthiban, et al., 2007) 13 Intelligent System CANFIS+ GeneticAlgorithm (Dessai, 2013) 13 Intelligent System Probabilistic Neural Network (Patil, 2014) 13 Intelligent System Naïve Bayes & Jelinek-mercer (Jarad, et al., 2015) 13 Intelligent System MONGODB

17 Proposed System Among 15 systems: Frequency Chest Pain Cholesterol HDL LDL Age Old Peak Age Sex Slope Smoking ECG Obesity Attribute Blood Pressure Heart Rate HbA1c Blood Sugar Major Vessels Exercise-induced angina Thallium Scan Calculating The Risk Level

18 Data sets (1/7) Age (year)

19 Data sets (1/7) Age (year)

20 Data sets (2/7) Blood Pressure (mmhg)

21 Data sets (2/7) Blood Pressure (mmhg)

22 Data sets (3/7) Heart Rate (bpm)

23 Data sets (3/7) Heart Rate (bpm)

24 Data sets (4/7) HDL (mg/dl ) High-Density Lipoprotein

25 Data sets (5/7) LDL (mg/dl ) Low-Density Lipoprotein

26 Data sets (4,5/7) HDL (mg/dl ) High-Density Lipoprotein LDL (mg/dl ) Low-Density Lipoprotein

27 Data sets (6/7) HbA1c (mmol/mol) Hemoglobin A1c

28 Data sets (6/7) HbA1c (mmol/mol) Hemoglobin A1c

29 Data sets (7/7) Chest Pain Type

30 Output Status

31 Output Crisp Value Fuzzy Value

32 Results Sample Results No. Input Variables Output Membership functions BP HR HDL LDL Age HbA1c ChestPain Result Status V. Health Health y y Low Med High Atypical Angina 9 Medium Risk Atypical Angina 11 High Risk No Pain 5 Low Risk No Pain -3 Very Healthy No Pain 1 Healthy Non-anginial Pain 4.3 Low Risk Atypical Angina 9.2 High Risk Atypical Angina 5.9 Medium Risk No Pain -3 Very Healthy Atypical Angina 8.6 Medium Risk

33 Other Methods No. Proposed Method Status Result Centroid Bisector MoM LoM SoM 1 Medium Risk High Risk Low Risk Very Healthy Healthy Low Risk High Risk Medium Risk Very Healthy Medium Risk

34 Other Methods Surface Viewer of Blood Pressure and Chest Pain (Centroid) Surface Viewer of Blood Pressure and Chest Pain (Bisector)

35 Other Methods Rule Viewer

36 Comparison Study Proposed Method Centroid Similarity% Bisector Similarity% Average % Average %

37 Future Work Compare different types of membership functions. Imply the possibility and probability theories. Intelligent System; involve Data Mining techniques. Extended to check more diseases.

38 Thank You