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1887

access icon free Combined mixed Gaussian model with pattern recognition in the automatic diagnosis of Alzheimer's disease

© The Institution of Engineering and Technology
Received 01/12/2019, Accepted 13/07/2020, Revised 19/06/2020, Published 16/12/2020

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.

Inspec keywords: pattern recognition; Gaussian processes; diseases; medical diagnostic computing; patient diagnosis; learning (artificial intelligence); cognition; statistical distributions

Other keywords: pattern recognition; computer-aided diagnosis; Alzheimer's disease prevention; patient cognition; combined mixed Gaussian model; classical machine learning; diagnostic system; probability prediction; automatic diagnosis; distribution methods

Subjects: Other topics in statistics; Machine learning (artificial intelligence); Biology and medical computing

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