Ensemble sparse intelligent mining techniques for cognitive disease

Currently, the number of individuals affected by cognitive disease is very high and the number is projected to increase tremendously by the year 2050, owing to escalating population aging. Recently, there has been a significant progress made by medical scientists concerning intelligent medical diagnostics with the emergence and advancement in ensemble sparse intelligent data mining techniques-based algorithms to diagnose cognitive diseases. Different sparse regression models, machine-learning techniques, deep learning methods are now being adopted for effective handling of high-dimensional data in various applications such as internet-of-things, artificial intelligence, decision making, big data, information, and communication technology. Because there are currently no effective treatments, early identification, as well as avoiding misdiagnosis, is crucial in providing a quality living for patients. As a result, medical specialists would benefit greatly from the introduction of computer-aided-diagnosis techniques. As a result, this chapter will provide a complete overview of recent breakthroughs in machine learning research, algorithms, and applications for forecasting cognitive illnesses, including Alzheimer's disease, Parkinson's disease, vascular dementia, and mild cognitive impairment in addition to hurdles, such as early diagnosis of cognitive illnesses and the incorporation of machine learning techniques into therapy planning and diagnostic practice.