Early Detection of Alzheimer's Disease Using Advanced Biomedical Materials and Nano-Enabled Biosensors: A Multimodal Approach

Abstract

Early diagnosis of Alzheimer's disease (AD) is critical in delaying disease progression and allows clinical management before the disease advances. Conventional diagnostic procedures, such as neuroimaging and laboratory biomarker analysis, are usually costly, invasive, and can only identify the disease in its severe stages. The paper proposes a multimodal diagnostic paradigm that relies on improved biomedical materials and nano-enabled biosensors to identify AD biomarkers at an early stage. The system combines an electrochemical biosensor that uses graphene and an optical biosensor that uses gold nanoparticles to identify major biomarkers such as amyloid-beta and tau proteins. The data received by the biosensors are discussed in the framework of multimodal data fusion and categorized with the help of a Support Vector Machine (SVM) algorithm. The experimental assessment based on a dataset of 700 biomarker samples proves that the suggested system has a detection accuracy of 94.6% as well as a sensitivity of 93.2 and a specificity of 95.1 in the datasets taken from Alzheimer's Disease Neuroimaging Initiative (ADNI). Analysis of the ROC curve shows an Area Under the Curve (AUC) of 0.96, indicating excellent classification performance. Those findings confirm the high validity of the claim that the combination of nanomaterials, biosensing technologies, and machine learning algorithms can enhance the reliability and effectiveness of early AD detection. The suggested framework has great potential for implementation in future miniature and point-of-care diagnostic devices.