Prediction of Alzheimer’s disease

Alzheimer’s disease assessment can reveal multiple pathophysiological lesions and changes concerning age, comorbidities, and dysregulation in nutritional intake as well as behavioral and cognitive abnormalities including dementia and mild cognitive impairment. Up to now, many potential biomarkers and risk factors have been presented as independent diagnostic guidelines, but none have yet suggested a standardized method with 100% prediction accuracy. Unfolded proteins, the overexpression of amyloid beta causing neuronal apoptosis and alteration in mitochondrial dynamics, the interaction of amyloid beta and tau proteins, and the neurofibrillary tangles strongly related to cognitive decline are commonly under investigation using cerebrospinal fluid analysis, structural and functional neuroimaging, and other electrophysiological tests related to Alzheimer’s disease. All these tests have supported the implementation of several tools for the early diagnosis or prediction of Alzheimer’s disease based mainly on computational tools like virtual physiological human models, expert decision support systems like geriatric assessment programs, statistical methods like Bayesian networks, or artificial intelligence algorithms. While there is no holistic treatment for Alzheimer’s disease or a single fingerprint test, early diagnosis and prediction of the disease is an efficient solution. This chapter focuses on the latest advances as well as various applications/tools for Alzheimer’s disease prediction.