Predictive modeling approaches for Alzheimer's disease diagnosis through neuroimaging techniques

Alzheimer's disease (AD) is one of the brain's neurodegenerative diseases. It is distinguished by a progressive mental, social, and behavioral deterioration. It affects the person's capacity, thinking, attention, reasoning, social behavior and functionality to achieve independence. The classical diagnosis process of AD consists of variety of neuroimaging scan approaches such as computerized tomography, magnetic resonance imaging and positron emission tomography. Classical cerebrospinal fluid biomarkers such as amyloid-β₄₂, total tau, and phosphorylated tau are used in conjunction with neuroimaging to diagnose Alzheimer's disease. Biomarkers are used inside of neuro scan images to measure the brain's structure and functions such as brain cortical thinning, brain atrophy and glucose metabolism. The Classical cerebrospinal fluid biomarkers such as amyloid-β₄₂, total tau, and phosphorylated tau are used in conjunction with neuroimaging to diagnose Alzheimer's disease. The classical diagnosis processes identified Alzheimer's disease through the manual manipulation of biomarkers in neuroimages. Therefore, the classical AD diagnosis processes suffer from time, cost and accuracy-related challenges. Machine learning and deep learning are the rising predictive modeling techniques that automatically diagnose AD with high accuracy and minimum time. The predictive modeling avoids manual manipulation of biomarkers and combines the processes of neuro scans and biomarkers. The integration of artificial intelligence with AD diagnoses addresses prevailing technological challenges in problem-solving and decision support. This study details and discusses the predictive modeling process and principal components with respect to the AD diagnosis process. The predictive model emphasizes the significance of diverse machine learning and deep learning algorithms. The predictive model utilized neuroimaging techniques, biomarker identification, features and data management, preprocessing, ML and DL algorithms, data sets, and performance matrices. This study also analyzes various classical predictive models and determines the performance level of the classifier, preprocessing steps, dataset, and validation metrics.