Heart Disease Prediction Using Deep Neural Networks: A Novel Approach

2021 ◽  
pp. 725-736
Author(s):  
Kondeth Fathima ◽  
E. R. Vimina
Keyword(s):  
Neural Networks ◽  
Heart Disease ◽  
Deep Neural Networks ◽  
Disease Prediction ◽  
Novel Approach

scholarly journals Single Cortical Neurons as Deep Artificial Neural Networks

2019 ◽  
Author(s):  
David Beniaguev ◽  
Idan Segev ◽  
Michael London
Keyword(s):  
Neural Networks ◽  
Cortical Neurons ◽  
Deep Neural Networks ◽  
Pyramidal Neurons ◽  
Classical Architecture ◽  
Novel Approach ◽  
Cortical Pyramidal Neurons ◽  
Spatio Temporal ◽  
Cortical Pyramidal Cell

AbstractWe introduce a novel approach to study neurons as sophisticated I/O information processing units by utilizing recent advances in the field of machine learning. We trained deep neural networks (DNNs) to mimic the I/O behavior of a detailed nonlinear model of a layer 5 cortical pyramidal cell, receiving rich spatio-temporal patterns of input synapse activations. A Temporally Convolutional DNN (TCN) with seven layers was required to accurately, and very efficiently, capture the I/O of this neuron at the millisecond resolution. This complexity primarily arises from local NMDA-based nonlinear dendritic conductances. The weight matrices of the DNN provide new insights into the I/O function of cortical pyramidal neurons, and the approach presented can provide a systematic characterization of the functional complexity of different neuron types. Our results demonstrate that cortical neurons can be conceptualized as multi-layered “deep” processing units, implying that the cortical networks they form have a non-classical architecture and are potentially more computationally powerful than previously assumed.

Analysis of Neural Networks Based Heart Disease Prediction System

2018 ◽  
Author(s):  
S.P. Rajamhoana ◽  
C. Akalya Devi ◽  
K. Umamaheswari ◽  
R. Kiruba ◽  
K. Karunya ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heart Disease ◽  
Disease Prediction ◽  
Prediction System

scholarly journals Invariant Representations through Adversarial Forgetting

2020 ◽  
Vol 34 (04) ◽  
pp. 4272-4279
Author(s):  
Ayush Jaiswal ◽  
Daniel Moyer ◽  
Greg Ver Steeg ◽  
Wael AbdAlmageed ◽  
Premkumar Natarajan
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Empirical Results ◽  
Information Bottleneck ◽  
Novel Approach ◽  
Adversarial Training ◽  
Invariant Representations ◽  
Art Performance ◽  
Forgetting Mechanism

We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechanism. We show that the forgetting mechanism serves as an information-bottleneck, which is manipulated by the adversarial training to learn invariance to unwanted factors. Empirical results show that the proposed framework achieves state-of-the-art performance at learning invariance in both nuisance and bias settings on a diverse collection of datasets and tasks.

scholarly journals Mutual Information Based Learning Rate Decay for Stochastic Gradient Descent Training of Deep Neural Networks

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 560
Author(s):  
Shrihari Vasudevan
Keyword(s):  
Neural Networks ◽  
Mutual Information ◽  
Gradient Descent ◽  
Deep Neural Networks ◽  
Learning Rate ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Novel Approach ◽  
The Neural Network ◽  
Gradient Based

This paper demonstrates a novel approach to training deep neural networks using a Mutual Information (MI)-driven, decaying Learning Rate (LR), Stochastic Gradient Descent (SGD) algorithm. MI between the output of the neural network and true outcomes is used to adaptively set the LR for the network, in every epoch of the training cycle. This idea is extended to layer-wise setting of LR, as MI naturally provides a layer-wise performance metric. A LR range test determining the operating LR range is also proposed. Experiments compared this approach with popular alternatives such as gradient-based adaptive LR algorithms like Adam, RMSprop, and LARS. Competitive to better accuracy outcomes obtained in competitive to better time, demonstrate the feasibility of the metric and approach.

scholarly journals Joint Optic Disc and Cup Segmentation Using Self-Supervised Multimodal Reconstruction Pre-Training

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 25
Author(s):  
Álvaro S. Hervella ◽  
Lucía Ramos ◽  
José Rouco ◽  
Jorge Novo ◽  
Marcos Ortega
Keyword(s):  
Neural Networks ◽  
Early Diagnosis ◽  
Optic Disc ◽  
Deep Neural Networks ◽  
The Self ◽  
Retinal Images ◽  
Novel Approach ◽  
Public Datasets

The analysis of the optic disc and cup in retinal images is important for the early diagnosis of glaucoma. In order to improve the joint segmentation of these relevant retinal structures, we propose a novel approach applying the self-supervised multimodal reconstruction of retinal images as pre-training for deep neural networks. The proposed approach is evaluated on different public datasets. The obtained results indicate that the self-supervised multimodal reconstruction pre-training improves the performance of the segmentation. Thus, the proposed approach presents a great potential for also improving the interpretable diagnosis of glaucoma.

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