NDLI: Combined mixed Gaussian model with pattern recognition in the automatic diagnosis of Alzheimer's disease

Content Provider	IET Digital Library
Author	Qian, Mingyue Zhang, Zhaoting Chen, Jiechun
Abstract	The diagnosis and prevention of Alzheimer's disease plays an important role in improving patient cognition. It can be seen from the current situation that the diagnosis of Alzheimer's disease is still poor because it is affected by many factors. Based on this, combined with the symptoms of Alzheimer's disease, this study used computer-aided to diagnose the symptoms of patients. First of all, this study analyses classical machine learning and chooses the appropriate model for diagnosis. Next, this study constructs a diagnostic system based on a mixed Gaussian model and uses a mixed Gaussian model to predict the probability of different distribution methods. Finally, this study designs experiments to analyse the performance of diagnostic models. Studies have shown that the mixed Gaussian model has a good effect on the automatic diagnosis of Alzheimer's disease, and can provide a theoretical reference for subsequent related research.
Starting Page	3698
Ending Page	3704
Page Count	7
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 15, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1629
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/15
Journal	IET Image Processing
Publisher Date	2020-12-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Alzheimer's Disease Prevention Automatic Diagnosis Biology And Medical Computing Classical Machine Learning Cognition Combined Mixed Gaussian Model Computer-aided Diagnosis Diagnostic System Diseases Distribution Method Gaussian Processes Learning in AI Medical Diagnostic Computing Patient Cognition Patient Diagnosis Pattern Recognition Probability Prediction Statistical Distribution Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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