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Chapter 15 A machine learning approach to voice analysis in Parkinson’s disease diagnosis

  • Jaya Singh , Ranjana Rajnish and Deepak Kumar Singh
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Digital Transformation in Healthcare 5.0
This chapter is in the book Digital Transformation in Healthcare 5.0

Abstract

Parkinson’s disease (PD) is characterized by a variety of motor and nonmotor symptoms and is a progressive neurodegenerative disorder. In the first stages of PD, a person may have trouble speaking or be unable to use their voice. Biomedical signal processing is now a major area of study. Therefore, diagnostic tools using voice analysis are urgently required since vocal impairment is among the first symptoms of PD. If PD can be diagnosed and treated early on, both the patient and their carers stand to benefit. Additionally, this may aid resource allocation at hospital administration hubs. The purpose of this research is to examine and compare the available machine learning techniques for effectively diagnosing PD. The dataset we analyzed is available in the UCI machine learning repository. The performance of several machine learning techniques is tested on this dataset. The suggested model achieves a 93% success rate on the test task when several learning models are stacked on top of one another.

Abstract

Parkinson’s disease (PD) is characterized by a variety of motor and nonmotor symptoms and is a progressive neurodegenerative disorder. In the first stages of PD, a person may have trouble speaking or be unable to use their voice. Biomedical signal processing is now a major area of study. Therefore, diagnostic tools using voice analysis are urgently required since vocal impairment is among the first symptoms of PD. If PD can be diagnosed and treated early on, both the patient and their carers stand to benefit. Additionally, this may aid resource allocation at hospital administration hubs. The purpose of this research is to examine and compare the available machine learning techniques for effectively diagnosing PD. The dataset we analyzed is available in the UCI machine learning repository. The performance of several machine learning techniques is tested on this dataset. The suggested model achieves a 93% success rate on the test task when several learning models are stacked on top of one another.

Chapters in this book

  1. Frontmatter I
  2. About the book V
  3. Preface VII
  4. Foreword IX
  5. Contents XI
  6. List of contributors XV
  7. Chapter 1 The impact of blockchain technology on the healthcare system 1
  8. Chapter 2 The role of metaverse in transforming healthcare: blockchain approach 33
  9. Chapter 3 Blockchain-empowered metaverse healthcare systems and applications 61
  10. Chapter 4 Role of artificial intelligence in disease diagnosis 89
  11. Chapter 5 Machine learning for twinning the human body 105
  12. Chapter 6 Improving patient care and healthcare management using bigdata analytics presents several research challenges 131
  13. Chapter 7 An emerging trends of bioinformatics and big data analytics in healthcare 159
  14. Chapter 8 Digital twins in medicine: leveraging machine learning for real-time diagnosis and treatment 189
  15. Chapter 9 Nanorobots in healthcare 209
  16. Chapter 10 Semantic-based approach for medical cyber-physical system (MCPS) with biometric authentication for secured privacy 237
  17. Chapter 11 Integration of cognitive computing and AI for smart healthcare 267
  18. Chapter 12 An overview of recommender systems in the healthcare domain: significant contributions, challenges, and future scope 293
  19. Chapter 13 Advancements and challenges of using natural language processing in the healthcare sector 317
  20. Chapter 14 Intraocular pressure monitoring system for glaucoma patients using IoT and machine learning 343
  21. Chapter 15 A machine learning approach to voice analysis in Parkinson’s disease diagnosis 365
  22. Index 375
https://doi.org/10.1515/9783111398549-015

Chapters in this book

  1. Frontmatter I
  2. About the book V
  3. Preface VII
  4. Foreword IX
  5. Contents XI
  6. List of contributors XV
  7. Chapter 1 The impact of blockchain technology on the healthcare system 1
  8. Chapter 2 The role of metaverse in transforming healthcare: blockchain approach 33
  9. Chapter 3 Blockchain-empowered metaverse healthcare systems and applications 61
  10. Chapter 4 Role of artificial intelligence in disease diagnosis 89
  11. Chapter 5 Machine learning for twinning the human body 105
  12. Chapter 6 Improving patient care and healthcare management using bigdata analytics presents several research challenges 131
  13. Chapter 7 An emerging trends of bioinformatics and big data analytics in healthcare 159
  14. Chapter 8 Digital twins in medicine: leveraging machine learning for real-time diagnosis and treatment 189
  15. Chapter 9 Nanorobots in healthcare 209
  16. Chapter 10 Semantic-based approach for medical cyber-physical system (MCPS) with biometric authentication for secured privacy 237
  17. Chapter 11 Integration of cognitive computing and AI for smart healthcare 267
  18. Chapter 12 An overview of recommender systems in the healthcare domain: significant contributions, challenges, and future scope 293
  19. Chapter 13 Advancements and challenges of using natural language processing in the healthcare sector 317
  20. Chapter 14 Intraocular pressure monitoring system for glaucoma patients using IoT and machine learning 343
  21. Chapter 15 A machine learning approach to voice analysis in Parkinson’s disease diagnosis 365
  22. Index 375
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