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Medical Diagnosis Using Artificial Neural Networks

Book Description

Advanced conceptual modeling techniques serve as a powerful tool for those in the medical field by increasing the accuracy and efficiency of the diagnostic process. The application of artificial intelligence assists medical professionals to analyze and comprehend a broad range of medical data, thus eliminating the potential for human error. Medical Diagnosis Using Artificial Neural Networks introduces effective parameters for improving the performance and application of machine learning and pattern recognition techniques to facilitate medical processes. This book is an essential reference work for academicians, professionals, researchers, and students interested in the relationship between artificial intelligence and medical science through the use of informatics to improve the quality of medical care.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Dedication
  5. Preface
    1. REFERENCES
  6. Acknowledgment
  7. Section 1: Introduction to Intelligent Medical Diagnosis
    1. Chapter 1: Medical Diagnosis
      1. ABSTRACT
      2. 1 INTRODUCTION
      3. 2 IMPORTANCE OF INTELLIGENT MEDICAL DIAGNOSIS
      4. 3 SUMMARY
      5. REFERENCES
      6. ADDITIONAL READING
    2. Chapter 2: Artificial Intelligence in Medical Science
      1. ABSTRACT
      2. 1 INTRODUCTION TO ARTIFICIAL INTELLIGENCE
      3. 2 APPLICATION OF ARTIFICIAL INTELLIGENCE FOR MEDICAL DIAGNOSIS
      4. 3 EXPERT SYSTEMS IN MEDICAL DIAGNOSIS
      5. 4 SUMMARY
      6. REFERENCES
    3. Chapter 3: Procedure of Medical Diagnosis
      1. ABSTRACT
      2. 1 PROCEDURE
      3. 2 MEDICAL DATA
      4. 3 SUMMARY
      5. REFERENCES
  8. Section 2: Artificial Neural Network
    1. Chapter 4: Definition of Artificial Neural Network
      1. ABSTRACT
      2. 1 BIOLOGICAL NEURAL NETWORK
      3. 2 DEFINITION OF ARTIFICIAL NEURAL NETWORK
      4. 3 MATHEMATICAL MODEL OF ARTIFICIAL NEURAL NETWORK
      5. 4 SUMMARY
      6. REFERENCES
    2. Chapter 5: Types of Artificial Neural Network
      1. ABSTRACT
      2. 1 FEEDFORWARD NEURAL NETWORK
      3. 2 RECURRENT NEURAL NETWORK
      4. 3 ARTIFICIAL NEURAL NETWORK ENSEMBLES
      5. 4 VALIDATING NEURAL NETWORKS
      6. 5 SUMMARY
      7. REFERENCES
    3. Chapter 6: Training Algorithms
      1. ABSTRACT
      2. 1 INTRODUCTION
      3. 2 SUPERVISED LEARNING
      4. 3 UNSUPERVISED LEARNING
      5. 4 SUMMARY
      6. REFERENCES
    4. Chapter 7: Artificial Neural Network for Medical Diagnosis
      1. ABSTRACT
      2. 1 APPLICATIONS OF ARTIFICIAL NEURAL NETWORK IN MEDICAL RESEARCH
      3. 2 EXAMPLES OF DISEASE DETECTED BY NEURAL NETWORK
      4. 3 SUMMARY
      5. REFERENCES
  9. Section 3: Heart Disorders Detection Using ANN
    1. Chapter 8: Introduction to Heart
      1. ABSTRACT
      2. 1 OVERVIEW
      3. 2 HEART FUNCTION
      4. 3 ELECTROCARDIOGRAM (ECG)
      5. 4 IMPORTANCE OF ELECTROCARDIOGRAPHY SIGNAL
      6. 5 COMMON HEART DISORDERS
      7. 6 PROBLEM STATEMENT
      8. 7 SCOPE
      9. 8 SUMMARY
      10. REFERENCES
    2. Chapter 9: Review on Heart Studies
      1. ABSTRACT
      2. 1 ECG SIGNAL NOISE REMOVAL
      3. 2 ANN SYSTEMS FOR ECG CLASSIFICATION
      4. 3 SWARM INTELLIGENCE FOR OPTIMIZATION
      5. 4 ECG SIGNAL FEATURE EXTRACTION
      6. 5 SUMMARY
      7. REFERENCES
    3. Chapter 10: Intelligent Detection of Heart Disorders
      1. ABSTRACT
      2. 1 INTRODUCTION
      3. 2 DATA COLLECTION
      4. 3 METHODOLOGY STAGES
      5. 4 SUMMARY
      6. REFERENCES
    4. Chapter 11: Feature Extraction and Classification
      1. ABSTRACT
      2. 1 INTRODUCTION
      3. 2 FEATURES OF DISORDERS
      4. 3 ECG SIGNAL CLASSIFICATION
      5. 4 RESULTS
      6. 5 SUMMARY
      7. REFERENCES
      8. ADDITIONAL READING
    5. Chapter 12: Other Diseases Detection
      1. ABSTRACT
      2. 1 INTRODUCTION
      3. 2 HEPATITIS
      4. 3 SUMMARY
      5. REFERENCES
  10. Section 4: Neural Network Optimization
    1. Chapter 13: Optimization Algorithms
      1. ABSTRACT
      2. 1 INTRODUCTION
      3. 2 PROBLEM BACKGROUND
      4. 3 OPTIMIZATION ALGORITHMS
      5. 4 COMPARISON BETWEEN PSO AND GA
      6. 5 PSONN VS. GANN
      7. 6 PSO PARAMETERS
      8. 7 GA-BASED NEURAL NETWORK
      9. 8 SUMMARY
      10. REFERENCES
    2. Chapter 14: ANN Optimization Experiments for Classification
      1. ABSTRACT
      2. 1 CLASSIFICATION PROBLEM
      3. 2 NEURAL NETWORK STRUCTURE FOR PSO-BASED NN AND GA-BASED NN
      4. 3 EXPERIMENTS
      5. 4 COMPARING THE PERFORMANCE OF PSONN AND GANN
      6. 5 DISCUSSION
      7. 6 OTHER OPTIMIZATION ALGORITHMS
      8. 7 SUMMARY
      9. REFERENCES
    3. Chapter 15: Netlab Training
      1. ABSTRACT
      2. 1 INTRODUCTION
      3. 2 FUNCTIONS
      4. 3 MORE EXAMPLES
      5. REFERENCES
  11. Appendix
    1. Appendix A
    2. Appendix B
    3. Appendix C
    4. Appendix D
  12. Glossary
  13. Related References
  14. Compilation of References
  15. About the Author