6. Predicting psychological disorders using machine learning
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Prabhsimar Kaur
Abstract
With the world population growing at a rapid pace, a large number of people are getting prone to chronic psychological disorders. The advancements in computer technology and machine learning approach are making healthcare more accessible and affordable for people. Early diagnosis of psychological disorders is also made possible with the aid of machine learning tools, thereby helping in improving a patient’s quality of life. In this section, we have tried to explain the classification of techniques in machine learning - supervised, unsupervised, and semisupervised - and how effectively these techniques are used in psychological disorder prediction. The accuracy levels of each technique have been reported. The recent advancements in machine learning techniques for predicting disorders have been explained. Also the challenges being faced while prediction of psychological disorders have been studied and explained in this chapter.
Abstract
With the world population growing at a rapid pace, a large number of people are getting prone to chronic psychological disorders. The advancements in computer technology and machine learning approach are making healthcare more accessible and affordable for people. Early diagnosis of psychological disorders is also made possible with the aid of machine learning tools, thereby helping in improving a patient’s quality of life. In this section, we have tried to explain the classification of techniques in machine learning - supervised, unsupervised, and semisupervised - and how effectively these techniques are used in psychological disorder prediction. The accuracy levels of each technique have been reported. The recent advancements in machine learning techniques for predicting disorders have been explained. Also the challenges being faced while prediction of psychological disorders have been studied and explained in this chapter.
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
