Non-Invasive Methods to Predict the Chances of Alzheimer’s Disease

Biomarker integration is an important and imperative process in the research of Alzheimer's condition, where non-invasive information gathered from various sources are integrated for more precise and earlier diagnosis. This paper investigates non-invasive biomarkers, such as MRI imaging, by using CNN and SVM machine learning models for the prediction of development of AD. For Neuroimaging the ADNI dataset is one of the most common datasets used in machine learning applications for Alzheimer's prediction. This work also evaluates the feasibility of other datasets, including the OASIS dataset, given the great value of imaging and clinical data it has. One promising direction in this domain is wearable data, where data collection could be non-invasive. This has enormous potential to enhance the diagnosis reliability and efficiency of non-invasive diagnosis of Alzheimer's. ADNI will have a significant part in the prediction of Alzheimer's due to the comprehensive longitudinal data it offers, the facilitation of biomarker identification, and large-scale data sharing to enable the development of precise early detection models. OASIS also offers complete longitudinal and cross-sectional neuroimaging data, and demographic information to facilitate collaborative research and validation of predictive models. These are indeed important data that will enable us to train powerful algorithms to permit early detection and prognosis for Alzheimer's disease. This study will contribute to extending current predictive methods by integrating these multimodal biomarkers and datasets and providing tools for non-invasive diagnosis in Alzheimer's disease.