Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry

Abstract Objectives To make a clear literature review on state-of-the-art heart disease prediction models. Methods It reviews 61 research papers and states the significant analysis. Initially, the analysis addresses the contributions of each literature works and observes the simulation environment. Here, different types of machine learning algorithms deployed in each contribution. In addition, the utilized dataset for existing heart disease prediction models was observed. Results The performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure, etc., are learned. Further, the best performance is also checked to confirm the effectiveness of entire contributions. Conclusions The comprehensive research challenges and the gap are portrayed based on the development of intelligent methods concerning the unresolved challenges in heart disease prediction using data mining techniques.

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Cognitive Performance in Parkinson’s Disease in the Brain Health Registry

Journal of Alzheimer s Disease ◽

10.3233/jad-181009 ◽

2019 ◽

Vol 68 (3) ◽

pp. 1029-1038 ◽

Cited By ~ 1

Author(s):

Brenna Cholerton ◽

Michael W. Weiner ◽

Rachel L. Nosheny ◽

Kathleen L. Poston ◽

R. Scott Mackin ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Performance ◽

Brain Health ◽

Health Registry ◽

The Brain

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Using Data Mining and Machine Learning Approaches to Observe Technology-Enhanced Learning

2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) ◽

10.1109/tale.2018.8615443 ◽

2018 ◽

Cited By ~ 2

Author(s):

Sarah K. Howard ◽

Jie Yang ◽

Jun Ma ◽

Chrisian Ritz ◽

Jiahong Zhao ◽

...

Keyword(s):

Machine Learning ◽

Data Mining ◽

Technology Enhanced Learning ◽

Learning Approaches ◽

Using Data ◽

Enhanced Learning

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Unsupervised online neuropsychological test performance for individuals with mild cognitive impairment and dementia: Results from the Brain Health Registry

Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring ◽

10.1016/j.dadm.2018.05.005 ◽

2018 ◽

Vol 10 (1) ◽

pp. 573-582 ◽

Cited By ~ 6

Author(s):

R. Scott Mackin ◽

Philip S. Insel ◽

Diana Truran ◽

Shannon Finley ◽

Derek Flenniken ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Test Performance ◽

Neuropsychological Test ◽

Brain Health ◽

Health Registry ◽

The Brain

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Diagnosis of Brain Diseases using Neural Networks

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b1036.1292s19 ◽

2019 ◽

Vol 9 (2S) ◽

pp. 402-407

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Neurological Diseases ◽

Brain Diseases ◽

Learning Approaches ◽

Learning Practices ◽

Computer Aided Analysis ◽

Computer Aided ◽

The Brain

Intensification in the occurrence of brain diseases and the need for the initial diagnosis for ailments like Tumor, Alzheimer’s, Epilepsy and Parkinson’s has riveted the attention of researchers. Machine learning practices, specifically deep learning, is considered as a beneficial diagnostic tool. Deep learning approaches to neuroimaging will assist computer-aided analysis of neurological diseases. Feature extraction of neuroimages carried out using Artificial Neural Networks leads to better diagnoses. In this study, all the brain diseases are revisited to consolidate the methodologies carried out by various authors in the literature.

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The ventral visual system

Brain Computations ◽

10.1093/oso/9780198871101.003.0002 ◽

2020 ◽

pp. 40-175

Author(s):

Edmund T. Rolls

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Visual Object ◽

Visual Object Recognition ◽

Learning Approaches ◽

Learning Mechanisms ◽

System P ◽

Inferior Temporal Visual Cortex ◽

The Brain ◽

Ventral Visual System

The brain processes involved in visual object recognition are described. Evidence is presented that what is computed are sparse distributed representations of objects that are invariant with respect to transforms including position, size, and even view in the ventral stream towards the inferior temporal visual cortex. Then biologically plausible unsupervised learning mechanisms that can perform this computation are described that use a synaptic modification rule what utilises a memory trace. These are compared with deep learning and other machine learning approaches that require supervision.

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Attempt of Early Stuck Detection Using Unsupervised Deep Learning With Probability Mixture Model

10.1115/omae2021-62739 ◽

2021 ◽

Author(s):

Tomoya Inoue ◽

Yujin Nakagawa ◽

Ryota Wada ◽

Keisuke Miyoshi ◽

Shungo Abe ◽

...

Keyword(s):

Machine Learning ◽

Early Detection ◽

Supervised Machine Learning ◽

Support Vector ◽

Learning Approaches ◽

Learning Models ◽

Vector Machines ◽

Unsupervised Deep Learning ◽

Drilling Operations ◽

Using Data

Abstract The early detection of a stuck pipe during drilling operations is challenging and crucial. Some of the studies on stuck detection have adopted supervised machine learning approaches with ordinal support vector machines or neural networks using datasets for “stuck” and “normal”. However, for early detection before stuck occurs, the application of ordinal supervised machine learning has several concerns, such as limited stuck data, lack of an exact “stuck sign” before it occurs, and the various mechanisms involved in pipe sticking. This study acquires surface drilling data from various wells belonging to several agencies, examines the effectiveness of multiple learning models, and discusses the possibility of the early detection of pipe sticking before it occurs. Unsupervised machine learning using data on the normal activities is a possible advanced method for early stuck detection, which is adopted in this study. In addition, as a countermeasure to another concern that even normal activities involve various operations, we apply unsupervised learning with multiple learning models.

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