Data-Driven Diagnostics and Disease Prediction with AI Optimization provides useful insights into model creation, data preparation, and ethical issues for healthcare applications. The book covers all the conventional and non-conventional methods related to this domain. It also discusses AI-based optimization techniques, Machine Learning models, and Advanced AI, offering practical insights, case studies, and optimization strategies to help data scientists and researchers efficiently employ AI in diagnostics and illness prediction in a world where precise diagnostics and early illness prediction may save lives and healthcare resources.
1. Chapter 1. Bridging data and diagnostics: an introduction to artificial intelligence-driven healthcare optimization - p. 1 2. Chapter 2. Artificial intelligence and healthcare: a critical review of the current state and future directions - p. 11 3. Chapter 3. Advancements in predictive modeling for early disease detection a comprehensive review and future prospects - p. 33 4. Chapter 4. Transforming healthcare through emergent technologies - p. 55 5. Chapter 5. Artificial intelligence in healthcare: improving diagnosis and treatment - p. 77 6. Chapter 6. Machine learning techniques in diagnosing and classifying disease detection - p. 95 7. Chapter 7. A new approach for healthcare management system:prediction of liver disease using various classification algorithms of machine learning and ensemble techniques - p. 117 8. Chapter 8. Cancer prediction using random forest convolutional neural network machine learning approach - p. 141 9. Chapter 9. Artificial intelligence in teledentistry and remote dental diagnostics: frameworks and conceptual models - p. 155 10. Chapter 10. User story clustering using K-means and K-medoids algorithms in Agile software development for healthcare system - p. 175 11. Chapter 11. Design of an explainable back propagation neural network for diabetes prediction - p. 193 12. Chapter 12. Detection of mental health and depression using deep learning - p. 219 13. Chapter 13. Artificial intelligence-driven job enlargement in healthcare: enhancing diagnostic efficiency and employee engagement - p. 241 14. Chapter 14. Predictive modeling for heart disease using machine learning: techniques, applications, and future directions - p. 261 15. Chapter 15. Deep learning approaches for brain tumor detection: techniques, challenges, and clinical applications - p. 285 16. Chapter 16. Detection of drowsiness using electrooculography and infrared sensor - p. 311 17. Chapter 17. Enhancing heart disease prediction with automated machine learning: a case study using EvalML - p. 323 18. Chapter 18. Advantages, drawbacks, emerging research challenges, and future scope in artificial intelligence-driven healthcare diagnostics and optimization - p. 339 19. Index - p. 345
Data-Driven Diagnostics and Disease Prediction with AI Optimization provides useful insights into model creation, data preparation, and ethical issues for healthcare applications. The book covers all the conventional and non-conventional methods related to this domain. It also discusses AI-based optimization techniques, Machine Learning models, and Advanced AI, offering practical insights, case studies, and optimization strategies to help data scientists and researchers efficiently employ AI in diagnostics and illness prediction in a world where precise diagnostics and early illness prediction may save lives and healthcare resources.
1. Chapter 1. Bridging data and diagnostics: an introduction to artificial intelligence-driven healthcare optimization - p. 1 2. Chapter 2. Artificial intelligence and healthcare: a critical review of the current state and future directions - p. 11 3. Chapter 3. Advancements in predictive modeling for early disease detection a comprehensive review and future prospects - p. 33 4. Chapter 4. Transforming healthcare through emergent technologies - p. 55 5. Chapter 5. Artificial intelligence in healthcare: improving diagnosis and treatment - p. 77 6. Chapter 6. Machine learning techniques in diagnosing and classifying disease detection - p. 95 7. Chapter 7. A new approach for healthcare management system:prediction of liver disease using various classification algorithms of machine learning and ensemble techniques - p. 117 8. Chapter 8. Cancer prediction using random forest convolutional neural network machine learning approach - p. 141 9. Chapter 9. Artificial intelligence in teledentistry and remote dental diagnostics: frameworks and conceptual models - p. 155 10. Chapter 10. User story clustering using K-means and K-medoids algorithms in Agile software development for healthcare system - p. 175 11. Chapter 11. Design of an explainable back propagation neural network for diabetes prediction - p. 193 12. Chapter 12. Detection of mental health and depression using deep learning - p. 219 13. Chapter 13. Artificial intelligence-driven job enlargement in healthcare: enhancing diagnostic efficiency and employee engagement - p. 241 14. Chapter 14. Predictive modeling for heart disease using machine learning: techniques, applications, and future directions - p. 261 15. Chapter 15. Deep learning approaches for brain tumor detection: techniques, challenges, and clinical applications - p. 285 16. Chapter 16. Detection of drowsiness using electrooculography and infrared sensor - p. 311 17. Chapter 17. Enhancing heart disease prediction with automated machine learning: a case study using EvalML - p. 323 18. Chapter 18. Advantages, drawbacks, emerging research challenges, and future scope in artificial intelligence-driven healthcare diagnostics and optimization - p. 339 19. Index - p. 345
