Comparative analysis of Alzheimer's disease classification by CDR level using CNN, feature selection, and machine‐learning techniques

2019 ◽  
Vol 29 (3) ◽  
pp. 297-310 ◽  
Author(s):  
Bijen Khagi ◽  
Goo‐Rak Kwon ◽  
Ramesh Lama
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Comparative Analysis ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Learning Techniques

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease

2020 ◽  
Vol 16 (1s) ◽  
pp. 1-35 ◽  
Author(s):  
M. Tanveer ◽  
B. Richhariya ◽  
R. U. Khan ◽  
A. H. Rashid ◽  
P. Khanna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Machine Learning Techniques ◽  
Learning Techniques

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

2016 ◽  
Vol 27 (8) ◽  
pp. 857-870 ◽  
Author(s):  
Golrokh Mirzaei ◽  
Anahita Adeli ◽  
Hojjat Adeli
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Probabilistic Neural Network ◽  
Machine Learning Techniques ◽  
Detection Techniques ◽  
The Past ◽  
Learning Techniques ◽  
Diagnosis Accuracy ◽  
Rate Research

AbstractAlzheimer’s disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.

scholarly journals Predicting Alzheimer’s disease progression trajectory and clinical subtypes using machine learning

2019 ◽  
Author(s):  
Vipul K. Satone ◽  
Rachneet Kaur ◽  
Anant Dadu ◽  
Hampton Leonard ◽  
Hirotaka Iwaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Disease Onset ◽  
Machine Learning Techniques ◽  
Progression Rate ◽  
Early Stages ◽  
Age Related ◽  
Learning Techniques

AbstractBackgroundAlzheimer’s disease (AD) is a common, age-related, neurodegenerative disease that impairs a person’s ability to perform day-to-day activities. Diagnosing AD is challenging, especially in the early stages. Many patients still go undiagnosed, partly due to the complex heterogeneity in disease progression. This highlights a need for early prediction of the disease course to assist its treatment and tailor therapy options to the disease progression rate. Recent developments in machine learning techniques provide the potential to not only predict disease progression and trajectory of AD but also to classify the disease into different etiological subtypes.Methods and findingsThe work shown here clusters participants in distinct and multifaceted progression subgroups of AD and discusses an approach to predict the progression rate from baseline diagnosis. We observed that the myriad of clinically reported symptoms summarized in the proposed AD progression space corresponds directly with memory and cognitive measures, which are routinely used to monitor disease onset and progression. Our analysis demonstrated accurate prediction of disease progression after four years from the first 12 months of post-diagnosis clinical data (Area Under the Curve of 0.96 (95% confidence interval (CI), 0.92-1.0), 0.81 (95% CI, 0.74-0.88) and 0.98 (95% CI, 0.96-1.0) for slow, moderate and fast progression rate patients respectively). Further, we explored the long short-term memory (LSTM) neural networks to predict the trajectory of an individual patient’s progression.ConclusionThe machine learning techniques presented in this study may assist providers in identifying different progression rates and trajectories in the early stages of the disease, hence allowing for more efficient and personalized healthcare deliveries. With additional information about the progression rate of AD at hand, providers may further individualize the treatment plans. The predictive tests discussed in this study not only allow for early AD diagnosis but also facilitate the characterization of distinct AD subtypes relating to trajectories of disease progression. These findings are a crucial step forward for early disease detection. These models can be used to design improved clinical trials for AD research.

scholarly journals Efficient Machine Learning Techniques to Diagnose and Predict Alzheimer’s disease

2020 ◽  
Vol 9 (3) ◽  
pp. 3953-3960
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Mechanisms ◽  
Learning Techniques

Recent research in computational engineering have evidenced the design and development numerous intelligent models to analyze medical data and derive inferences related to early diagnosis and prediction of disease severity. In this context, prediction and diagnosis of fatal neurodegenerative diseases that comes under the class of dementia from medical image data is considered as the challenging area of research for many researchers. Recently Alzheimer’s disease is considered as major category of dementia that affects major population. Despite of the development of numerous machine learning models for early diagnosis of Alzheimer’s disease, it is observed that there is a lot more scope of research. Addressing the same, this article presents a systematic literature review of machine learning techniques developed for early diagnosis of Alzheimer’s disease. Furthermore this article includes major categories of machine learning algorithms that include artificial neural networks, Support vector machines and Deep learning based ensemble models that helps the budding researchers to explore the scope of research in predicting Alzheimer’s disease. Implementation results depict the comparative analysis of state of art machine learning mechanisms.

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