Alzheimer’s Disease Classification Using Machine Learning Algorithms

2019 ◽  
pp. 117-134 ◽  
Author(s):  
S. Naganandhini ◽  
P. Shanmugavadivu ◽  
A. Asaithambi ◽  
M. Mohammed Mansoor Roomi
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Classification

scholarly journals Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer’s Disease

2021 ◽  
Vol 22 (5) ◽  
pp. 2761
Author(s):  
Chun-Hung Chang ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Support Vector ◽  
Cochrane Central Register ◽  
Novel Biomarkers ◽  
Multiple Variables

Background: Alzheimer’s disease (AD) is a complex and severe neurodegenerative disease that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on cognitive tests, imaging techniques and cerebrospinal fluid (CSF) levels of amyloid-β1-42 (Aβ42), total tau protein and hyperphosphorylated tau (p-tau). However, the available methods are expensive and relatively invasive. Artificial intelligence techniques like machine learning tools have being increasingly used in precision diagnosis. Methods: We conducted a meta-analysis to investigate the machine learning and novel biomarkers for the diagnosis of AD. Methods: We searched PubMed, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews for reviews and trials that investigated the machine learning and novel biomarkers in diagnosis of AD. Results: In additional to Aβ and tau-related biomarkers, biomarkers according to other mechanisms of AD pathology have been investigated. Neuronal injury biomarker includes neurofiliament light (NFL). Biomarkers about synaptic dysfunction and/or loss includes neurogranin, BACE1, synaptotagmin, SNAP-25, GAP-43, synaptophysin. Biomarkers about neuroinflammation includes sTREM2, and YKL-40. Besides, d-glutamate is one of coagonists at the NMDARs. Several machine learning algorithms including support vector machine, logistic regression, random forest, and naïve Bayes) to build an optimal predictive model to distinguish patients with AD from healthy controls. Conclusions: Our results revealed machine learning with novel biomarkers and multiple variables may increase the sensitivity and specificity in diagnosis of AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing AD in outpatient clinics.

Olfactory Phenotypes Differentiate Cognitively Unimpaired Seniors from Alzheimer’s Disease and Mild Cognitive Impairment: A Combined Machine Learning and Traditional Statistical Approach

2021 ◽  
pp. 1-10
Author(s):  
Jennifer Li ◽  
Andres M. Bur ◽  
Mark R. Villwock ◽  
Suraj Shankar ◽  
Gracie Palmer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Subjective Data ◽  
Disease States ◽  
Olfactory Testing

Background: Olfactory dysfunction (OD) is an early symptom of Alzheimer’s disease (AD). However, olfactory testing is not commonly performed to test OD in the setting of AD. Objective: This work investigates objective OD as a non-invasive biomarker for accurately classifying subjects as cognitively unimpaired (CU), mild cognitive impairment (MCI), and AD. Methods: Patients with MCI (n = 24) and AD (n = 24), and CU (n = 33) controls completed two objective tests of olfaction (Affordable, Rapid, Olfactory Measurement Array –AROMA; Sniffin’ Sticks Screening 12 Test –SST12). Demographic and subjective sinonasal and olfaction symptom information was also obtained. Analyses utilized traditional statistics and machine learning to determine olfactory variables, and combinations of variables, of importance for differentiating normal and disease states. Results: Inability to correctly identify a scent after detection was a hallmark of MCI/AD. AROMA was superior to SST12 for differentiating MCI from AD. Performance on the clove scent was significantly different between all three groups. AROMA regression modeling yielded six scents with AUC of the ROC of 0.890 (p <  0.001). Random forest model machine learning algorithms considering AROMA olfactory data successfully predicted MCI versus AD disease state. Considering only AROMA data, machine learning algorithms were 87.5%accurate (95%CI 0.4735, 0.9968). Sensitivity and specificity were 100%and 75%, respectively with ROC of 0.875. When considering AROMA and subject demographic and subjective data, the AUC of the ROC increased to 0.9375. Conclusion: OD differentiates CUs from those with MCI and AD and can accurately predict MCI versus AD. Leveraging OD data may meaningfully guide management and research decisions.

ADVANCING ALZHEIMER’S DISEASE TREATMENT: LESSONS FROM CTAD 2018

2019 ◽  
pp. 1-6
Author(s):  
B. Vellas ◽  
L.J. Bain ◽  
J. Touchon ◽  
P.S. Aisen
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Predictive Modeling ◽  
Learning Algorithms ◽  
Synaptic Function ◽  
Machine Learning Algorithms ◽  
Disease Treatment ◽  
Efficiency And Productivity

The 2018 Clinical Trials on Alzheimer’s Disease (CTAD) conference showcased recent successes and failures in trials of Alzheimer’s disease treatments. More importantly, the conference provided opportunities for investigators to share what they have learned from those studies with the goal of designing future trials with a greater likelihood of success. Data from studies of novel and non-amyloid treatment approaches were also shared, including neuroprotective and regenerative strategies and those that target neuroinflammation and synaptic function. New tools to improve the efficiency and productivity of clinical trials were described, including biomarkers and machine learning algorithms for predictive modeling.

scholarly journals Machine Learning Approach for Automatic Detection of Alzheimers Disease using Resting State fMRI

2021 ◽  
Vol 9 (VI) ◽  
pp. 5399-5409
Author(s):  
K. Emily Esther Rani
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Resting State ◽  
Learning Algorithms ◽  
Resting State Fmri ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Discrete Wavelet

Alzheimer’s Disease (AD) is a neurological disease that affects memory and the livelihood of the people that are diagnosed with it. Efficient automated techniques for early diagnosis of AD is very important because early diagnosis is used to prevent a patient from death. In this work, we present a novel computer-aided diagnosis (CAD) techniques using machine learning algorithms for the early diagnosis of AD. The input resting state fMRI(rsfMRI) images are taken from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The input image is pre-processed using Discrete Wavelet Transform(DWT). Automated thresholding algorithm is used to segment the image. Then, the segmented resting state fMRI images are used to extract useful and informative features. The best features are selected by Fisher’s code feature selection algorithm. Finally, an automated Image classification step is performed using machine learning algorithms Support Vector Machine(SVM), Decision Tree , Random Forest and Multi-Layer Perceptron algorithms to distinguish between normal patients and AD patients.

scholarly journals A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease

2020 ◽  
Vol 12 ◽  
Author(s):  
Ibrahim Almubark ◽  
Lin-Ching Chang ◽  
Kyle F. Shattuck ◽  
Thanh Nguyen ◽  
Raymond Scott Turner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Neuropsychological Tests ◽  
Cognitive Task ◽  
Learning Algorithms ◽  
High Specificity ◽  
Machine Learning Algorithms ◽  
Cognitive Data

Introduction: The goal of this study was to investigate and compare the classification performance of machine learning with behavioral data from standard neuropsychological tests, a cognitive task, or both.Methods: A neuropsychological battery and a simple 5-min cognitive task were administered to eight individuals with mild cognitive impairment (MCI), eight individuals with mild Alzheimer's disease (AD), and 41 demographically match controls (CN). A fully connected multilayer perceptron (MLP) network and four supervised traditional machine learning algorithms were used.Results: Traditional machine learning algorithms achieved similar classification performances with neuropsychological or cognitive data. MLP outperformed traditional algorithms with the cognitive data (either alone or together with neuropsychological data), but not neuropsychological data. In particularly, MLP with a combination of summarized scores from neuropsychological tests and the cognitive task achieved ~90% sensitivity and ~90% specificity. Applying the models to an independent dataset, in which the participants were demographically different from the ones in the main dataset, a high specificity was maintained (100%), but the sensitivity was dropped to 66.67%.Discussion: Deep learning with data from specific cognitive task(s) holds promise for assisting in the early diagnosis of Alzheimer's disease, but future work with a large and diverse sample is necessary to validate and to improve this approach.

Sign in / Sign up

Export Citation Format

Share Document