Author:
Singhania Udit,Tripathy Balakrushna,Hasan Mohammad Kamrul,Anumbe Noble C.,Alboaneen Dabiah,Ahmed Fatima Rayan Awad,Ahmed Thowiba E.,Nour Manasik M. Mohamed
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.
Funder
Universiti Kebangsaan Malaysia
Subject
Public Health, Environmental and Occupational Health
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献