14. Machine learning in healthcare
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Daiyaan Ahmed Shaik
Abstract
Machine learning (ML) is an application of AI (artificial intelligence), which deals with the study of capability of a computer to learn from the given data to gain knowledge in making predictions and decisions based on its experience. Such technology can benefit healthcare industry to a great extent. It is the fastest growing industry with high rates of progress in the field of health with new technologies emerging rapidly. These can be extended to a wide range of clinical tasks and prediction tasks since the performance of ML algorithms has been proved to be more than that of humans. Nowadays, all of the patient data has been recorded on computers, and the existing patient data can be used by the doctors and examiners for follow-ups. ML algorithms use this existing data and analyze them to identify patterns that are used to make precise diagnosis and provide better care to patients. With the invention of wearables, all the patient data has been monitored and stored, which is then used by ML for better patient management. ML algorithms are also being used to accurately predict the progress of a disease. This innovation can give chances to improve the proficiency and quality of healthcare.
Abstract
Machine learning (ML) is an application of AI (artificial intelligence), which deals with the study of capability of a computer to learn from the given data to gain knowledge in making predictions and decisions based on its experience. Such technology can benefit healthcare industry to a great extent. It is the fastest growing industry with high rates of progress in the field of health with new technologies emerging rapidly. These can be extended to a wide range of clinical tasks and prediction tasks since the performance of ML algorithms has been proved to be more than that of humans. Nowadays, all of the patient data has been recorded on computers, and the existing patient data can be used by the doctors and examiners for follow-ups. ML algorithms use this existing data and analyze them to identify patterns that are used to make precise diagnosis and provide better care to patients. With the invention of wearables, all the patient data has been monitored and stored, which is then used by ML for better patient management. ML algorithms are also being used to accurately predict the progress of a disease. This innovation can give chances to improve the proficiency and quality of healthcare.
Chapters in this book
- Frontmatter I
- Preface VII
- Contents XI
- List of contributors XIII
- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction 1
- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope 21
- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM 35
- 4. Computational intelligence approach to address the language barrier in healthcare 53
- 5. Recent advancement of machine learning and deep learning in the field of healthcare system 77
- 6. Predicting psychological disorders using machine learning 99
- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines 131
- 8. Machine learning approach for exploring computational intelligence 153
- 9. Classification of various image fusion algorithms and their performance evaluation metrics 179
- 10. Recommender system in healthcare: an overview 199
- 11. Dense CNN approach for medical diagnosis 217
- 12. Impact of sentiment analysis tools to improve patients’ life in critical diseases 239
- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm 253
- 14. Machine learning in healthcare 277
- 15. Computational health informatics using evolutionary-based feature selection 309
- Index 329
Chapters in this book
- Frontmatter I
- Preface VII
- Contents XI
- List of contributors XIII
- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction 1
- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope 21
- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM 35
- 4. Computational intelligence approach to address the language barrier in healthcare 53
- 5. Recent advancement of machine learning and deep learning in the field of healthcare system 77
- 6. Predicting psychological disorders using machine learning 99
- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines 131
- 8. Machine learning approach for exploring computational intelligence 153
- 9. Classification of various image fusion algorithms and their performance evaluation metrics 179
- 10. Recommender system in healthcare: an overview 199
- 11. Dense CNN approach for medical diagnosis 217
- 12. Impact of sentiment analysis tools to improve patients’ life in critical diseases 239
- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm 253
- 14. Machine learning in healthcare 277
- 15. Computational health informatics using evolutionary-based feature selection 309
- Index 329
