Predicting Alzheimer's disease from Clinical Dementia Rating, Estimated total intracranial volume & Atlas Scaling Factor : Incorporating Ensemble approach into Automated Machine Learning

The current state-of-the-art for automated machine learning is adopted to predict Alzheimer's disease (AD) by adopting variables such as Mini Mental State Examination score, estimated total intracranial volume and Atlas Scaling Factor. A macro-weighted average Area under the Response-operating Curve of 0.96 is achieved with a close-to-perfect AD detection score after incorporating the ensemble approach. Such predictive models shall serve to optimize risk stratification and management protocols for this enfeebling ailment.

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Predicting Clinical Dementia Rating Using Blood RNA Levels

10.1101/19009092 ◽

2019 ◽

Author(s):

Justin B. Miller ◽

John S.K. Kauwe ◽

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Microarray Data ◽

Predictive Accuracy ◽

Cognitive Status ◽

Clinical Dementia Rating ◽

P Values ◽

Intracellular Channel ◽

Rna Levels

Structured AbstractINTRODUCTIONThe Clinical Dementia Rating (CDR) is commonly used to assess cognitive decline in Alzheimer’s disease patients.METHODSWe divided 741 participants with blood microarray data in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) into three groups based on their most recent CDR assessment: cognitive normal (CDR=0), mild dementia (CDR=0.5), and probable AD (CDR≥1.0). We then used machine learning to predict cognitive status using only blood RNA levels.RESULTSOne chloride intracellular channel 1 (CLIC1) probe was significant. By combining nonsignificant probes with p-values less than 0.1, we averaged 87.87 (s = 1.02)% predictive accuracy in classifying the three groups, compared to a 55.46% baseline for this study.DISCUSSIONWe identified one significant probe in CLIC1. However, CLIC1 levels alone were not sufficient to determine dementia status. We propose that combining individually suggestive, but nonsignificant, blood RNA levels can significantly improve diagnostic accuracy.

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Alzheimer’s disease risk prediction using automated machine learning

Alzheimer s & Dementia ◽

10.1002/alz.053953 ◽

2021 ◽

Vol 17 (S5) ◽

Author(s):

Xiaoyi Raymond Gao ◽

Yi‐Ju Li ◽

Eden R. Martin

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Risk Prediction ◽

Disease Risk ◽

Automated Machine Learning

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Accurate Blood-Based Diagnostic Biosignatures for Alzheimer’s Disease via Automated Machine Learning

Journal of Clinical Medicine ◽

10.3390/jcm9093016 ◽

2020 ◽

Vol 9 (9) ◽

pp. 3016

Author(s):

Makrina Karaglani ◽

Krystallia Gourlia ◽

Ioannis Tsamardinos ◽

Ekaterini Chatzaki

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Performance Metrics ◽

Biomarker Discovery ◽

Support Vector ◽

Neurodegenerative Dementia ◽

Proteomic Dataset ◽

Automated Machine Learning ◽

Via Classification

Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics datasets from studies in AD blood, by the AutoML technology Just Add Data Bio (JADBIO), to construct accurate predictive models for use as diagnostic biosignatures. Considering data from AD patients and age–sex matched cognitively healthy individuals, we produced three best performing diagnostic biosignatures specific for the presence of AD: A. A 506-feature transcriptomic dataset from 48 AD and 22 controls led to a miRNA-based biosignature via Support Vector Machines with three miRNA predictors (AUC 0.975 (0.906, 1.000)), B. A 38,327-feature transcriptomic dataset from 134 AD and 100 controls led to six mRNA-based statistically equivalent signatures via Classification Random Forests with 25 mRNA predictors (AUC 0.846 (0.778, 0.905)) and C. A 9483-feature proteomic dataset from 25 AD and 37 controls led to a protein-based biosignature via Ridge Logistic Regression with seven protein predictors (AUC 0.921 (0.849, 0.972)). These performance metrics were also validated through the JADBIO pipeline confirming stability. In conclusion, using the automated machine learning tool JADBIO, we produced accurate predictive biosignatures extrapolating available low sample -omics data. These results offer options for minimally invasive blood-based diagnostic tests for AD, awaiting clinical validation based on respective laboratory assays. They also highlight the value of AutoML in biomarker discovery.

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Predicting Clinical Dementia Rating Using Blood RNA Levels

Genes ◽

10.3390/genes11060706 ◽

2020 ◽

Vol 11 (6) ◽

pp. 706

Author(s):

Justin B. Miller ◽

John S. K. Kauwe

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Operating Characteristic ◽

Predictive Accuracy ◽

Cognitive Status ◽

Clinical Dementia Rating ◽

Intracellular Channel ◽

Rna Levels

The Clinical Dementia Rating (CDR) is commonly used to assess cognitive decline in Alzheimer’s disease patients and is included in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We divided 741 ADNI participants with blood microarray data into three groups based on their most recent CDR assessment: cognitive normal (CDR = 0), mild cognitive impairment (CDR = 0.5), and probable Alzheimer’s disease (CDR ≥ 1.0). We then used machine learning to predict cognitive status using only blood RNA levels. Only one probe for chloride intracellular channel 1 (CLIC1) was significant after correction. However, by combining individually nonsignificant probes with p-values less than 0.1, we averaged 87.87% (s = 1.02) predictive accuracy for classifying the three groups, compared to a 55.46% baseline for this study due to unequal group sizes. The best model had an overall precision of 0.902, recall of 0.895, and a receiver operating characteristic (ROC) curve area of 0.904. Although we identified one significant probe in CLIC1, CLIC1 levels alone were not sufficient to predict dementia status and cannot be used alone in a clinical setting. Additional analyses combining individually suggestive, but nonsignificant, blood RNA levels were significantly predictive and may improve diagnostic accuracy for Alzheimer’s disease. Therefore, we propose that patient features that do not individually predict cognitive status might still contribute to overall cognitive decline through interactions that can be elucidated through machine learning.

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Alzheimer's Disease Brain Areas: The Machine Learning Support for Blind Localization

Current Alzheimer Research ◽

10.2174/1567205013666160314144822 ◽

2016 ◽

Vol 13 (5) ◽

pp. 498-508 ◽

Cited By ~ 2

Author(s):

V. Vigneron ◽

A. Kodewitz ◽

A. M. Tome ◽

S. Lelandais ◽

E. Lang

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Learning Support ◽

Brain Areas

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Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽

10.3390/pr8091071 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1071

Author(s):

Lucia Billeci ◽

Asia Badolato ◽

Lorenzo Bachi ◽

Alessandro Tonacci

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Diffusion Tensor Imaging ◽

Magnetic Resonance ◽

Diffusion Tensor ◽

Classification Problem ◽

Computer Algorithms ◽

Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

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