A Predictive and Preventive Model for Onset of Alzheimer's Disease

Abstract

Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several lifestyle changes that affect biological functions. Detection of AD at its early stages helps in the treatment of patients. In this paper, a predictive and preventive model that uses biomarkers such as the amyloid-beta protein is proposed to detect, predict, and prevent AD onset. A Convolution Neural Network (CNN) based model is developed to predict AD at its early stages. The results obtained proved that the proposed model outperforms the traditional Machine Learning (ML) algorithms such as Logistic Regression, Support Vector Machine, Decision Tree Classifier, and K Nearest Neighbor algorithms.

Lipidomic markers for the prediction of progression from mild cognitive impairment to Alzheimer's disease

“…5,6 For example, Udit Singhania and colleagues reported a predictive model based on brain Aβ which can predict AD risk with high accuracy (c-index = 0.904). 6 However, these biomarkers are either invasive or too expensive for pre-clinical conditions. Compared with CSF biomarkers, blood biomarkers are more readily accessible for elders.…”

“…Prediction of AD is commonly based on the image of a brain scan or the analysis of cerebrospinal fluid (CSF). 5,6 For example, Udit Singhania and colleagues reported a predictive model based on brain Aβ which can predict AD risk with high accuracy (c-index = 0.904). 6 However, these biomarkers are either invasive or too expensive for pre-clinical conditions.…”