5. Recent advancement of machine learning and deep learning in the field of healthcare system
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Yogesh Kumar
Abstract
The healthcare sector has long been adapted primarily and significantly from scientific advances. Nowadays, machine learning (ML, a subset of artificial intelligence) plays a vital role in numerous health-related domains, including the expansion of novel medical measures, managing patient information and records, and treatment of chronic ailments. ML in medicine has recently made headlines. Google has developed an ML technique to help recognize cancerous tumors on mammograms. Stanford uses a deep learning method to classify skin cancer diseases. Still, ML advances itself to developments better than other terminologies. Algorithms can deliver instant advantage to disciplines with procedures that are reproducible or consistent. Also, those with huge number of medical image datasets, such as radiology, pathology, and cardiology, are robust aspirants. ML can be qualified to look at images, classify irregularities, and opinion to parts that require attention, thus improving the correctness of all these developments. In future, ML will provide benefits to the family physician at home. ML can also offer an objective opinion to improve productivity, consistency, and accurateness. Healthcare needs to interchange from intelligence of ML as an innovative perception to sight it as a real-world tool that can be organized nowadays. We must take an incremental approach if ML has to play a role in healthcare system. ML has boundless impression in the area of healthcare such as drug discovery applications, robotic surgery, predicting diabetics, liver abnormality, and also in personalized healthcare. The main aim of the chapter is to study the advancement of ML in recent healthcare applications such as automatic treatment or recommendation for different diseases, automatic robotic surgery, drug discovery and development, and other latest domains of the healthcare system. The chapter also comprises the analysis of different ML techniques used in healthcare. Another objective of the chapter provides a systematic procedure to use ML techniques on healthcare domains. The hybrid ML methods can also be used to detect different types of diseases. Using ML algorithms, the efficient system that identifies multicancer diseases can be developed at the same time. The chapter also comprises the analysis based on ML methods and deep learning methods in healthcare system.
Abstract
The healthcare sector has long been adapted primarily and significantly from scientific advances. Nowadays, machine learning (ML, a subset of artificial intelligence) plays a vital role in numerous health-related domains, including the expansion of novel medical measures, managing patient information and records, and treatment of chronic ailments. ML in medicine has recently made headlines. Google has developed an ML technique to help recognize cancerous tumors on mammograms. Stanford uses a deep learning method to classify skin cancer diseases. Still, ML advances itself to developments better than other terminologies. Algorithms can deliver instant advantage to disciplines with procedures that are reproducible or consistent. Also, those with huge number of medical image datasets, such as radiology, pathology, and cardiology, are robust aspirants. ML can be qualified to look at images, classify irregularities, and opinion to parts that require attention, thus improving the correctness of all these developments. In future, ML will provide benefits to the family physician at home. ML can also offer an objective opinion to improve productivity, consistency, and accurateness. Healthcare needs to interchange from intelligence of ML as an innovative perception to sight it as a real-world tool that can be organized nowadays. We must take an incremental approach if ML has to play a role in healthcare system. ML has boundless impression in the area of healthcare such as drug discovery applications, robotic surgery, predicting diabetics, liver abnormality, and also in personalized healthcare. The main aim of the chapter is to study the advancement of ML in recent healthcare applications such as automatic treatment or recommendation for different diseases, automatic robotic surgery, drug discovery and development, and other latest domains of the healthcare system. The chapter also comprises the analysis of different ML techniques used in healthcare. Another objective of the chapter provides a systematic procedure to use ML techniques on healthcare domains. The hybrid ML methods can also be used to detect different types of diseases. Using ML algorithms, the efficient system that identifies multicancer diseases can be developed at the same time. The chapter also comprises the analysis based on ML methods and deep learning methods in healthcare system.
Kapitel in diesem Buch
- 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
Kapitel in diesem Buch
- 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
