Analysing Risk of Coronary Heart Disease through Discriminative Neural Networks

• URL: http://arxiv.org/abs/2008.02731v1
• Date: Wed, 17 Jun 2020 06:30:00 GMT
• Title: Analysing Risk of Coronary Heart Disease through Discriminative Neural Networks
• Authors: Ayush Khaneja, Siddharth Srivastava, Astha Rai, A S Cheema, P K Srivastava
• Abstract summary: In critical applications like diagnostics, this class imbalance cannot be overlooked. We depict how we can handle this class imbalance through neural networks using a discriminative model and contrastive loss.
• Score: 18.124078832445967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The application of data mining, machine learning and artificial intelligence techniques in the field of diagnostics is not a new concept, and these techniques have been very successfully applied in a variety of applications, especially in dermatology and cancer research. But, in the case of medical problems that involve tests resulting in true or false (binary classification), the data generally has a class imbalance with samples majorly belonging to one class (ex: a patient undergoes a regular test and the results are false). Such disparity in data causes problems when trying to model predictive systems on the data. In critical applications like diagnostics, this class imbalance cannot be overlooked and must be given extra attention. In our research, we depict how we can handle this class imbalance through neural networks using a discriminative model and contrastive loss using a Siamese neural network structure. Such a model does not work on a probability-based approach to classify samples into labels. Instead it uses a distance-based approach to differentiate between samples classified under different labels. The code is available at https://tinyurl.com/DiscriminativeCHD/

Related papers

• An Autoencoder and Generative Adversarial Networks Approach for Multi-Omics Data Imbalanced Class Handling and Classification [2.2940141855172036]
  In molecular biology, there has been an explosion of data generated from multi-omics sequencing. Traditional statistical methods face challenging tasks when dealing with such high dimensional data. This study, focused on tackling these challenges in a neural network that incorporates autoencoder to extract latent space of the features.
  arXiv  Detail & Related papers  (2024-05-16T01:45:55Z)
• Comparison of Machine Learning Classification Algorithms and Application to the Framingham Heart Study [0.0]
  The use of machine learning algorithms in healthcare can amplify social injustices and health inequities. This research pertains to some generalizability impediments that occur during the development and the post-deployment of machine learning classification algorithms.
  arXiv  Detail & Related papers  (2024-02-22T22:49:35Z)
• Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study [49.5374512525016]
  Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. Some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images. We propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance.
  arXiv  Detail & Related papers  (2024-01-18T16:59:27Z)
• Neuroevolutionary algorithms driven by neuron coverage metrics for semi-supervised classification [60.60571130467197]
  In some machine learning applications the availability of labeled instances for supervised classification is limited while unlabeled instances are abundant. We introduce neuroevolutionary approaches that exploit unlabeled instances by using neuron coverage metrics computed on the neural network architecture encoded by each candidate solution.
  arXiv  Detail & Related papers  (2023-03-05T23:38:44Z)
• Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models [50.537859423741644]
  Training a model on an imbalanced dataset can introduce unique challenges to the learning problem. We look deeper into the internal units of neural networks to observe how handling data imbalance affects the learned features.
  arXiv  Detail & Related papers  (2022-04-04T09:38:38Z)
• Algorithmic encoding of protected characteristics and its implications on disparities across subgroups [17.415882865534638]
  Machine learning models may pick up undesirable correlations between a patient's racial identity and clinical outcome. Very little is known about how these biases are encoded and how one may reduce or even remove disparate performance.
  arXiv  Detail & Related papers  (2021-10-27T20:30:57Z)
• RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data [47.00510780034136]
  Class imbalance is a familiar issue in the field of disease prediction. In this paper, we propose Re-weighted Adversarial Graph Convolutional Network (RA-GCN) to enhance the performance of the graph-based classifier. We show the superiority of RA-GCN on synthetic and three publicly available medical datasets compared to the recent method.
  arXiv  Detail & Related papers  (2021-02-27T14:06:27Z)
• Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View [82.80085730891126]
  We provide the first modernally precise analysis of linear multiclass classification. Our analysis reveals that the classification accuracy is highly distribution-dependent. The insights gained may pave the way for a precise understanding of other classification algorithms.
  arXiv  Detail & Related papers  (2020-11-16T05:17:29Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Outlier Detection through Null Space Analysis of Neural Networks [3.220347094114561]
  We use the concept of the null space to integrate an outlier detection method directly into a neural network used for classification. Our method, called Null Space Analysis (NuSA) of neural networks, works by computing and controlling the magnitude of the null space projection as data is passed through a network. Results are shown that indicate networks trained with NuSA retain their classification performance while also being able to detect outliers at rates similar to commonly used outlier detection algorithms.
  arXiv  Detail & Related papers  (2020-07-02T17:17:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.