2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope
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Divya Gaba
Abstract
Health informatics primarily means dealing with the methodologies that help to acquire, store, and use information in health and medicine. Large database including heterogeneous and complex data of healthcare can be very beneficial but it is difficult for humans to interpret such a “big data.” With such a large dataset, machine learning (ML) algorithms can work very well in predicting the disease and treatment methods. ML algorithms include learning from past experience and making the predictions and decisions for the current problems. There are many challenges that are encountered while applying ML in healthcare and mostly in healthcare applications where dataset is very complex and is of varied type (ranging from texts to scans). A possible alternative is the use of interactive ML where doctors can be taken in loop. Hence, an integrated and extensive approach is required for application of ML in health informatics. This chapter deals with the various types of ML techniques, approaches, challenges, and its future scope in healthcare informatics. Further, these techniques can be used to make a model for quick and precise healthcare discovery.
Abstract
Health informatics primarily means dealing with the methodologies that help to acquire, store, and use information in health and medicine. Large database including heterogeneous and complex data of healthcare can be very beneficial but it is difficult for humans to interpret such a “big data.” With such a large dataset, machine learning (ML) algorithms can work very well in predicting the disease and treatment methods. ML algorithms include learning from past experience and making the predictions and decisions for the current problems. There are many challenges that are encountered while applying ML in healthcare and mostly in healthcare applications where dataset is very complex and is of varied type (ranging from texts to scans). A possible alternative is the use of interactive ML where doctors can be taken in loop. Hence, an integrated and extensive approach is required for application of ML in health informatics. This chapter deals with the various types of ML techniques, approaches, challenges, and its future scope in healthcare informatics. Further, these techniques can be used to make a model for quick and precise healthcare discovery.
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
