scholarly journals A Novel Two-Stage Heart Arrhythmia Ensemble Classifier

Computers ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 60
Author(s):  
Mercedeh J. Rezaei ◽  
John R. Woodward ◽  
Julia Ramírez ◽  
Patricia Munroe
Keyword(s):  
Class Imbalance ◽  
Automatic Detection ◽  
Classification Performance ◽  
Healthy Population ◽  
Stage Model ◽  
Uk Biobank ◽  
Two Stage ◽  
Heart Arrhythmia ◽  
The Uk

Atrial fibrillation (AF) and ventricular arrhythmia (Arr) are among the most common and fatal cardiac arrhythmias in the world. Electrocardiogram (ECG) data, collected as part of the UK Biobank, represents an opportunity for analysis and classification of these two diseases in the UK. The main objective of our study is to investigate a two-stage model for the classification of individuals with AF and Arr in the UK Biobank dataset. The current literature addresses heart arrhythmia classification very extensively. However, the data used by most researchers lack enough instances of these common diseases. Moreover, by proposing the two-stage model and separation of normal and abnormal cases, we have improved the performance of the classifiers in detection of each specific disease. Our approach consists of two stages of classification. In the first stage, features of the ECG input are classified into two main classes: normal and abnormal. At the second stage, the features of the ECG are further categorised as abnormal and further classified into two diseases of AF and Arr. A diverse set of ECG features such as the QRS duration, PR interval and RR interval, as well as covariates such as sex, BMI, age and other factors, are used in the modelling process. For both stages, we use the XGBoost Classifier algorithm. The healthy population present in the data, has been undersampled to tackle the class imbalance present in the data. This technique has been applied and evaluated using an ECG dataset from the UKBioBank ECG taken at rest repository. The main results of our paper are as follows: The classification performance for the proposed approach has been measured using F1 score, Sensitivity (Recall) and Specificity (Precision). The results of the proposed system are 87.22%, 88.55% and 85.95%, for average F1 Score, average sensitivity and average specificity, respectively. Contribution and significance: The performance level indicates that automatic detection of AF and Arr in participants present in the UK Biobank is more precise and efficient if done in a two-stage manner. Automatic detection and classification of AF and Arr individuals this way would mean early diagnosis and prevention of more serious consequences later in their lives.

A two-stage clustering-based cold-start method for active learning

2021 ◽  
Vol 25 (5) ◽  
pp. 1169-1185
Author(s):  
Deniu He ◽  
Hong Yu ◽  
Guoyin Wang ◽  
Jie Li
Keyword(s):  
Active Learning ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Cold Start ◽  
Classification Performance ◽  
The Novel ◽  
Two Stage ◽  
Minority Class ◽  
Novel Method ◽  
Multiple Clusters

The problem of initialization of active learning is considered in this paper. Especially, this paper studies the problem in an imbalanced data scenario, which is called as class-imbalance active learning cold-start. The novel method is two-stage clustering-based active learning cold-start (ALCS). In the first stage, to separate the instances of minority class from that of majority class, a multi-center clustering is constructed based on a new inter-cluster tightness measure, thus the data is grouped into multiple clusters. Then, in the second stage, the initial training instances are selected from each cluster based on an adaptive candidate representative instances determination mechanism and a clusters-cyclic instance query mechanism. The comprehensive experiments demonstrate the effectiveness of the proposed method from the aspects of class coverage, classification performance, and impact on active learning.

scholarly journals Can a data driven obesity classification system identify those at risk of severe COVID-19 in the UK Biobank cohort study?

2021 ◽  
Author(s):  
Stephen Clark ◽  
Michelle Morris ◽  
Nik Lomax ◽  
Mark Birkin
Keyword(s):  
Cohort Study ◽  
United Kingdom ◽  
Classification System ◽  
Holistic Approach ◽  
Data Driven ◽  
Uk Biobank ◽  
The United Kingdom ◽  
The Individual ◽  
The Uk

AbstractCOVID-19 is a disease that has been shown to have outcomes that vary by certain socio-demographic and socio-economic groups. It is increasingly important that an understanding of these outcomes should be derived not from the consideration of one aspect, but by a more multi-faceted understanding of the individual. In this study use is made of a recent obesity driven classification of participants in the United Kingdom Biobank (UKB) to identify trends in COVID-19 outcomes. This classification is informed by a recently created obesity systems map, and the COVID-19 outcomes are: undertaking a test, a positive test, hospitalisation and mortality. It is demonstrated that the classification is able to identify meaningful differentials in these outcomes. This more holistic approach is recommended for identification and prioritisation of COVID-19 risk and possible long-COVID determination.

scholarly journals Automatic Detection and Classification of Focal Liver Lesions Based on Deep Convolutional Neural Networks: A Preliminary Study

2021 ◽  
Vol 10 ◽  
Author(s):  
Jiarong Zhou ◽  
Wenzhe Wang ◽  
Biwen Lei ◽  
Wenhao Ge ◽  
Yu Huang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Detection ◽  
Classification Performance ◽  
Focal Liver Lesions ◽  
Liver Lesions ◽  
Deep Convolutional Neural Networks ◽  
Average Accuracy ◽  
Preliminary Study

With the increasing daily workload of physicians, computer-aided diagnosis (CAD) systems based on deep learning play an increasingly important role in pattern recognition of diagnostic medical images. In this paper, we propose a framework based on hierarchical convolutional neural networks (CNNs) for automatic detection and classification of focal liver lesions (FLLs) in multi-phasic computed tomography (CT). A total of 616 nodules, composed of three types of malignant lesions (hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and metastasis) and benign lesions (hemangioma, focal nodular hyperplasia, and cyst), were randomly divided into training and test sets at an approximate ratio of 3:1. To evaluate the performance of our model, other commonly adopted CNN models and two physicians were included for comparison. Our model achieved the best results to detect FLLs, with an average test precision of 82.8%, recall of 93.4%, and F1-score of 87.8%. Our model initially classified FLLs into malignant and benign and then classified them into more detailed classes. For the binary and six-class classification, our model achieved average accuracy results of 82.5 and73.4%, respectively, which were better than the other three classification neural networks. Interestingly, the classification performance of the model was placed between a junior physician and a senior physician. Overall, this preliminary study demonstrates that our proposed multi-modality and multi-scale CNN structure can locate and classify FLLs accurately in a limited dataset, and would help inexperienced physicians to reach a diagnosis in clinical practice.

scholarly journals Imbalanced learning: Improving classification of diabetic neuropathy from magnetic resonance imaging

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243907
Author(s):  
Kevin Teh ◽  
Paul Armitage ◽  
Solomon Tesfaye ◽  
Dinesh Selvarajah ◽  
Iain D. Wilkinson
Keyword(s):  
Magnetic Resonance Imaging ◽  
Support Vector Machine ◽  
Class Imbalance ◽  
Nearest Neighbors ◽  
Classification Performance ◽  
Support Vector ◽  
Imbalanced Learning ◽  
Resonance Imaging ◽  
K Nearest Neighbors

One of the fundamental challenges when dealing with medical imaging datasets is class imbalance. Class imbalance happens where an instance in the class of interest is relatively low, when compared to the rest of the data. This study aims to apply oversampling strategies in an attempt to balance the classes and improve classification performance. We evaluated four different classifiers from k-nearest neighbors (k-NN), support vector machine (SVM), multilayer perceptron (MLP) and decision trees (DT) with 73 oversampling strategies. In this work, we used imbalanced learning oversampling techniques to improve classification in datasets that are distinctively sparser and clustered. This work reports the best oversampling and classifier combinations and concludes that the usage of oversampling methods always outperforms no oversampling strategies hence improving the classification results.

scholarly journals Identifying healthy individuals with Alzheimer neuroimaging phenotypes in the UK Biobank

2022 ◽  
Author(s):  
Tiago Azevedo ◽  
Richard A.I. Bethlehem ◽  
David J. Whiteside ◽  
Nol Swaddiwudhipong ◽  
James B. Rowe ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disease ◽  
Fluid Intelligence ◽  
Structural Mri ◽  
Critical Issue ◽  
Healthy Population ◽  
Uk Biobank ◽  
Sporadic Disease ◽  
The Uk

Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer's disease (AD) in particular, to identify populations suitable for preventive and early disease modifying trials. Evidence from genetic studies suggest the neurodegeneration of Alzheimer's disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be detected in sporadic disease. To address this challenge we train a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD-score representing the probability of AD using structural MRI data in the Alzheimer's Disease Neuroimaging Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real world dataset of the National Alzheimer's Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80), and demonstrate correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer's disease. This cohort have a cognitive profile in keeping with Alzheimer's disease, with strong evidence for poorer fluid intelligence, and with some evidence of poorer performance on tests of numeric memory, reaction time, working memory and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking. This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer's disease.

scholarly journals Microglial-expressed genetic risk variants, cognitive function and brain volume in patients with schizophrenia and healthy controls

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Emma Corley ◽  
Laurena Holleran ◽  
Laura Fahey ◽  
Aiden Corvin ◽  
Derek W. Morris ◽  
...  
Keyword(s):  
Cognitive Functioning ◽  
Grey Matter ◽  
Grey Matter Volume ◽  
Healthy Population ◽  
Uk Biobank ◽  
Polygenic Score ◽  
Risk Variants ◽  
Polygenic Scores ◽  
Show Evidence ◽  
The Uk

AbstractChanges in immune function are associated with variance in cognitive functioning in schizophrenia. Given that microglia are the primary innate immune cells in the brain, we examined whether schizophrenia risk-associated microglial genes (measured via polygenic score analysis) explained variation in cognition in patients with schizophrenia and controls (n = 1,238) and tested whether grey matter mediated this association. We further sought to replicate these associations in an independent sample of UK Biobank participants (n = 134,827). We then compared the strength of these microglial associations to that of neuronal and astroglial (i.e., other brain-expressed genes) polygenic scores, and used MAGMA to test for enrichment of these gene-sets with schizophrenia risk. Increased microglial schizophrenia polygenic risk was associated with significantly lower performance across several measures of cognitive functioning in both samples; associations which were then found to be mediated via total grey matter volume in the UK Biobank. Unlike neuronal genes which did show evidence of enrichment, the microglial gene-set was not significantly enriched for schizophrenia, suggesting that the relevance of microglia may be for neurodevelopmental processes related more generally to cognition. Further, the microglial polygenic score was associated with performance on a range of cognitive measures in a manner comparable to the neuronal schizophrenia polygenic score, with fewer cognitive associations observed for the astroglial score. In conclusion, our study supports the growing evidence of the importance of immune processes to understanding cognition and brain structure in both patients and in the healthy population.

Timing of repeated movements: Test of a two-stage model

1997 ◽  
Author(s):  
Saul Sternberg ◽  
Teresa Pantzer
Keyword(s):  
Stage Model ◽  
Two Stage

Low birthweight is associated with poorer limb muscle mass and grip strength in middle age: findings from the UK Biobank Imaging Enhancement

2019 ◽  
Author(s):  
Elizabeth Curtis ◽  
Justin Liu ◽  
Kate Ward ◽  
Karen Jameson ◽  
Zahra Raisi-Estabragh ◽  
...  
Keyword(s):  
Grip Strength ◽  
Muscle Mass ◽  
Low Birthweight ◽  
Middle Age ◽  
Limb Muscle ◽  
Uk Biobank ◽  
The Uk
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