Applied Machine Learning to Identify Alzheimer's Disease through the Analysis of Magnetic Resonance Imaging

2015 ◽  
Author(s):  
Elva Maria Novoa-Del-Toro ◽  
Hector Gabriel Acosta-Mesa ◽  
Juan Fernandez-Ruiz ◽  
Nicandro Cruz-Ramirez
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Applied Machine Learning

scholarly journals A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

2020 ◽  
Vol 9 (7) ◽  
pp. 2146
Author(s):  
Gopi Battineni ◽  
Nalini Chintalapudi ◽  
Francesco Amenta ◽  
Enea Traini
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Magnetic Resonance ◽  
Neurodegenerative Disorder ◽  
Support Vector ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage.

Prevalence of Sinusitis Detected by Magnetic Resonance Imaging in Subjects with Dementia or Alzheimer’s Disease

2015 ◽  
Vol 12 (10) ◽  
pp. 1006-1011 ◽  
Author(s):  
Minori Yasue ◽  
Saiko Sugiura ◽  
Yasue Uchida ◽  
Hironao Otake ◽  
Masaaki Teranishi ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Resonance Imaging

scholarly journals Quantitative sodium imaging using ultra‐high field magnetic resonance imaging in patients with Alzheimer's disease

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Alexa Haeger ◽  
Arthur Coste ◽  
Cécile Lerman‐Rabrait ◽  
Julien Lagarde ◽  
Jörg B. Schulz ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
High Field ◽  
Resonance Imaging ◽  
Ultra High Field ◽  
Sodium Imaging
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