Developing an Artificial Intelligence Framework for Identifying Fusion Blood-Based Biomarkers in Alzheimer's Disease

Alzheimer’s Disease (AD) is an irreversible neurological disorder, a major cause of disability among the elderly, with no effective therapeutic options currently available. It is an asymp-tomatic disease in the prodromal stages and begins many years before clinical appearances. Early diagnosis of AD allows patients to obtain appropriate healthcare assistance, accelerating the development of new medications. A biomarker that evaluates the alterations in the brain cells produced by AD in its preliminary periods might be significant for its early identification. Blood-based biomarkers (BBBMs) facilitate the early detection of AD. The BBBMs detection procedure is cost-efficient and minimally invasive. The aim of this study is to identify the best BBBMs, and machine learning (ML) algorithms play a significant role in identifying people at the high-risk of AD. A total of 146 BBBMs from a database by ADNI, and 12-ML algorithms were investigated. The results show that linear discriminant analysis, Naive Bayes, and support vector machine are the promising ML algorithms for AD detection that integrated into the novel ensemble voting detection model. Furthermore, the four BBBMs i.e., Immunoglobulin M (IGM), Placenta Growth Factor (PLGF), Serum Glutamic Oxaloacetic Transaminase (SGOT), and Alpha-1-Microglobulin (A1Micro) are the significant biomarkers to detect AD in its early stages with performance of 92.86% for sensitivity and 82.35% for specificity. Consequently, BBBMs are the preferred option in clinical practice. In addition, integrating artificial intelli-gence such as ML into healthcare might help with early detection of AD.