AIMC Topic: Discriminant Analysis

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Automated detection and classification of diabetes disease based on Bangladesh demographic and health survey data, 2011 using machine learning approach.

Diabetes & metabolic syndrome Mar 10, 2020
BACKGROUND AND AIMS: Diabetes has been recognized as a continuing health challenge for the twenty-first century, both in developed and developing countries including Bangladesh. The main objective of this study is to use machine learning (ML) based c...
Discriminant Analysis Automation Bangladesh Demography Female Diabetes Mellitus Support Vector Machine Factor Analysis, Statistical Area Under Curve Humans Machine Learning Middle Aged Health Surveys Diabetes Mellitus, Type 2 Logistic Models Treatment Outcome Reproducibility of Results Adult Male
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Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets.

Scientific reports Feb 24, 2020
One of the most interesting and everyday natural phenomenon is the formation of different patterns after the evaporation of liquid droplets on a solid surface. The analysis of dried patterns from blood droplets has recently gained a lot of attention,...
Humans Image Processing, Computer-Assisted Principal Component Analysis Discriminant Analysis Machine Learning Blood Chemical Analysis Algorithms Dried Blood Spot Testing Young Adult Male Exercise
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Migrating from partial least squares discriminant analysis to artificial neural networks: a comparison of functionally equivalent visualisation and feature contribution tools using jupyter notebooks.

Metabolomics : Official journal of the Metabolomic Society Jan 21, 2020
INTRODUCTION: Metabolomics data is commonly modelled multivariately using partial least squares discriminant analysis (PLS-DA). Its success is primarily due to ease of interpretation, through projection to latent structures, and transparent assessmen...
Metabolomics Neural Networks, Computer Least-Squares Analysis Discriminant Analysis Software
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A Classification Algorithm by Combination of Feature Decomposition and Kernel Discriminant Analysis (KDA) for Automatic MR Brain Image Classification and AD Diagnosis.

Computational and mathematical methods in medicine Dec 30, 2019
Magnetic resonance (MR) imaging is a widely used imaging modality for detection of brain anatomical variations caused by brain diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). AD considered as an irreversible neurodegene...
Neuroimaging Positron-Emission Tomography Image Interpretation, Computer-Assisted Male Linear Models Biomarkers Middle Aged Pattern Recognition, Automated Brain Disease Progression Female Diagnosis, Computer-Assisted Reproducibility of Results Cognitive Dysfunction Humans Aged Image Processing, Computer-Assisted Machine Learning Magnetic Resonance Imaging Algorithms Discriminant Analysis Databases, Factual Alzheimer Disease
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Sparse Ensemble Machine Learning to Improve Robustness of Long-Term Decoding in iBMIs.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Dec 27, 2019
This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracortical Brain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the en...
Machine Learning Algorithms Animals Male Support Vector Machine Neural Networks, Computer Cerebral Cortex Macaca fascicularis Discriminant Analysis Signal Processing, Computer-Assisted Psychomotor Performance Brain-Computer Interfaces
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Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification.

Scientific reports Dec 20, 2019
Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping...
Machine Learning Algorithms Support Vector Machine Discriminant Analysis Data Mining
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Incremental Learning to Personalize Human Activity Recognition Models: The Importance of Human AI Collaboration.

Sensors (Basel, Switzerland) Nov 25, 2019
This study presents incremental learning based methods to personalize human activity recognition models. Initially, a user-independent model is used in the recognition process. When a new user starts to use the human activity recognition application,...
Discriminant Analysis Supervised Machine Learning Human Activities Humans Artificial Intelligence Algorithms
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The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior.

The Journal of neuroscience : the official journal of the Society for Neuroscience Nov 21, 2019
Identifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding population coding. Statistical features, such as tuning properties, individual and ...
Neural Networks, Computer Female Animals Male Prefrontal Cortex Models, Neurological Motion Perception Behavior, Animal Discriminant Analysis Visual Perception Neurons Macaca fascicularis Attention Macaca mulatta Psychomotor Performance Temporal Lobe
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Celiac disease diagnosis from videocapsule endoscopy images with residual learning and deep feature extraction.

Computer methods and programs in biomedicine Nov 20, 2019
BACKGROUND AND OBJECTIVE: Videocapsule endoscopy (VCE) is a relatively new technique for evaluating the presence of villous atrophy in celiac disease patients. The diagnostic analysis of video frames is currently time-consuming and tedious. Recently,...
Capsule Endoscopy Celiac Disease Video Recording Humans Image Interpretation, Computer-Assisted Machine Learning Linear Models Algorithms Calibration Reproducibility of Results Image Processing, Computer-Assisted Endoscopy Support Vector Machine Sensitivity and Specificity Intestinal Mucosa Diagnosis, Computer-Assisted Discriminant Analysis Deep Learning Light
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Prediction of pulmonary pressure after Glenn shunts by computed tomography-based machine learning models.

European radiology Nov 8, 2019
OBJECTIVES: This study aimed to develop non-invasive machine learning classifiers for predicting post-Glenn shunt patients with low and high risks of a mean pulmonary arterial pressure (mPAP) > 15 mmHg based on preoperative cardiac computed tomograph...
Pulmonary Artery Heart Septal Defects Univentricular Heart Humans Machine Learning Bayes Theorem Algorithms Prognosis Discriminant Analysis Retrospective Studies Double Outlet Right Ventricle Tomography, X-Ray Computed Fontan Procedure Child, Preschool Female Heart Defects, Congenital Child Young Adult Pulmonary Atresia Male Logistic Models Tricuspid Atresia Support Vector Machine Cardiac Catheterization Infant Blood Pressure Transposition of Great Vessels Lung Adolescent
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