TITLE:
Forecasting Alzheimer’s Disease Using Combination Model Based on Machine Learning
AUTHORS:
He Li, Yuhang Wu, Yingnan Zhang, Tao Wei, Yufeng Gui
KEYWORDS:
Alzheimer’s Disease, BP Neural Network, SVM, Random Forest, Combination Forecasting Model
JOURNAL NAME:
Applied Mathematics,
Vol.9 No.4,
April
30,
2018
ABSTRACT: As the acceleration of aged population tendency, building models to forecast
Alzheimer’s Disease (AD) is essential. In this article, we surveyed 1157 interviewees.
By analyzing the results using three machine learning methods—BP
neural network, SVM and random forest, we can derive the accuracy of them
in forecasting AD, so that we can compare the methods in solving AD prediction.
Among them, random forest is the most accurate method. Moreover, to
combine the advantages of the methods, we build a new combination forecasting
model based on the three machine learning models, which is proved
more accurate than the models singly. At last, we give the conclusion of the
connection between life style and AD, and provide several suggestions for elderly
people to help them prevent AD.