Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin (Type I or insulin-dependent diabetes) or when the body cannot effectively use the insulin it produces (Type II or non-insulin dependent diabetes). Insulin is a hormone that regulates Blood Glucose Levels (BGLs). The global number of people with diabetes is now 463 million and is expected to rise to over 700 million by 2045. The risk of life- threatening complications, due to both hypoglycaemia (low BGLs) and hyperglycaemia (high BGLs), may be reduced using appropriate technological innovations, and technology products on the market are proving essential to delay or even prevent serious complications, such as blindness, kidney failure, heart attacks, stroke, and lower limb amputation, developing in the long-term. The global health expenditure for treating diabetes in 2019 was over USD 760 billion. Electronic BGL meters, insulin delivery systems including “pens” and insulin pumps, retinal screening procedures involving advanced imaging technologies, software advice packages and the artificial pancreas, are now playing a major role in revolutionizing day-to-day management and improving the prospects for many people with diabetes.

This book proposal will showcase new developments in Computational Intelligence using Machine Learning (ML), Deep Learning (DL), Explainable Artificial Intelligence (XAI), Quantum Machine Learning (QML), Medical IoT, Body Sensors, and Mobile Health based diagnosis assisted Decision Support Systems (DSS) for improving diabetes healthcare ecosystem. In effect, it should be of interest to anyone living with diabetes, clinicians who work in diabetes healthcare, and engineers and technologists who are involved with the mass screening and effective tool for diabetes technology industry.