A major use of practical predictive analytics in medicine has been in the diagnosis of current diseases, particularly through medical imaging. Now there is sufficient improvement in AI, IoT and data analytics to deal with real time problems with an increased focus on early prediction using machine learning and deep learning algorithms. With the power of artificial intelligence alongside the internet of 'medical' things, these algorithms can input the characteristics/data of their patients and get predictions of future diagnoses, classifications, treatment and costs.

• Chapter 1: COVID-19 detection in X-ray images using customized CNN model
• Chapter 2: Introducing deep learning in medical diagnosis
• Chapter 3: Intelligent approach for network intrusion detection system (NIDS) utilizing machine learning (ML)
• Chapter 4: Classification methodologies in healthcare
• Chapter 5: Introducing deep learning in medical domain
• Chapter 6: Deep-stacked autoencoder for medical image classification
• Chapter 7: Comparison of machine learning and deep learning algorithms for prediction of coronary heart disease
• Chapter 8: Revolution in technology-enabled healthcare: Internet of Things
• Chapter 9: Smart healthcare monitoring framework using IoT with big data analytics
• Chapter 10: Experimental analysis and investigation of dementia detection framework using EHR-based variant LSTM model
• Chapter 11: An intelligent agent-based distributed patient scheduling using token-based coordination approach: a case study
• Chapter 12: Internet of Things (IoT) for the efficient healthcare system
• Chapter 13: Comprehension of melody representation and speed-up approaches for query by humming system
• Chapter 14: Python for digital health solutions: elevated outcomes
• Chapter 15: IoT-enabled healthcare - a paradigm shift
• Chapter 16: IoT-based cardiovascular prediction framework using deep learning algorithms
• Chapter 17: An intelligent approach using convolutional neural network (CNN) for early detection of melanoma and other skin diseases
• Chapter 18: Self-organizing deep learning approach for controlling movements of wheeled apparatus through corneal connotation
• Chapter 19: Prediction of breast tumour outcome to chemotherapy using statistical MR images through deep learning approaches
• Chapter 20: Risk analysis and prediction of cancer associated with Type II diabetes: a review