Addressing Concept Drifts Using Deep Learning for Heart Disease Prediction: A Review

2021 ◽  
pp. 157-167
Author(s):  
Ketan Sanjay Desale ◽  
Swati V. Shinde
Keyword(s):  
Heart Disease ◽  
Deep Learning ◽  
Disease Prediction ◽  
Concept Drifts

scholarly journals Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

2021 ◽  
Vol 11 (6) ◽  
pp. 5467
Author(s):  
Surenthiran Krishnan ◽  
Pritheega Magalingam ◽  
Roslina Ibrahim
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Learning Model ◽  
Disease Prediction ◽  
The Neural Network ◽  
Proposed Model ◽  
Deep Learning Model

<span>This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.</span>

scholarly journals An Ensemble Deep Dynamic Algorithm (EDDA) to Predict the Heart Disease

2021 ◽  
pp. 105-111
Author(s):  
J. Nageswara Rao ◽  
R. Satya Prasad
Keyword(s):  
Heart Disease ◽  
Deep Learning ◽  
Disease Prediction ◽  
Dynamic Algorithm ◽  
Risk Of Death ◽  
Deep Boltzmann Machine ◽  
Comparative Results ◽  
Sensitivity Specificity ◽  
Accuracy Of Prediction ◽  
Automatic Systems

Nowadays heart disease becomes more complicated to every human being. Machine Learning and Deep Learning plays the major role in processing the automatic systems. Prediction of heart disease is most difficult task because many algorithms perform limited operations. The aim of the paper is to increase the accuracy and prediction values. Various heart disease datasets are available for the research. Deep Learning (DL) algorithms play the major role in prediction of heart disease. Prediction can be done in the early stages to reduce the risk of death for the humans. In this paper, An Ensemble Deep Dynamic Algorithm (EDDA) is introduced to increase the accuracy of prediction values. The EDDA follows the some steps to process the prediction of heart disease. The steps are as follows: Linear Regression and Deep Boltzmann Machine (DBM) is applied on the selected dataset. Performance is calculated in terms of sensitivity, specificity and accuracy are shown with the comparative results.

scholarly journals A Genetic Algorithm based Hybrid Deep Learning Approach for Heart Disease Prediction

2021 ◽  
Vol 16 (3) ◽  
Author(s):  
Khushbu Verma ◽  
Ankit Singh Bartwal ◽  
Mathura Prasad Thapliyal
Keyword(s):  
Genetic Algorithm ◽  
Heart Disease ◽  
Deep Learning ◽  
Early Stage ◽  
Support Vector ◽  
Disease Prediction ◽  
The Past ◽  
Lifestyle Choices ◽  
Proposed Model ◽  
Tree Algorithms

People nowadays suffer from a variety of heart ailments as a result of the environment and their lifestyle choices. As a result, analyzing sickness at an early stage becomes a critical responsibility. Data mining uses disease data to uncover important knowledge. In this research paper, we employ the hybrid combination of a Genetic Algorithm based Feature selection and Ensemble Deep Neural Network Model for Heart Disease prediction. In this algorithm, we used a 0.04 learning rate and Adam optimizer was used for enhancement of the proposed model. The proposed algorithm has come to 98% accuracy of heart disease prediction, which is higher than the past approaches. Other exist models such as random forest, logistic regression, support vector machine, Decision tree algorithms have taken a higher time and give less accuracy compare to the proposed hybrid deep learning-based approach.

scholarly journals Automatic Heart Disease Classification Using Ensemble Features Extraction Mechanism from ECG Signals

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 790-805
Author(s):  
Avinash L. Golande ◽  
T. Pavankumar
Keyword(s):  
Heart Disease ◽  
Deep Learning ◽  
Cost Effective ◽  
Disease Classification ◽  
Disease Prediction ◽  
Prediction System ◽  
Healthcare Applications ◽  
Hybrid Features ◽  
Smart Healthcare ◽  
Hybrid Mechanism

The heart disease detection and classification using the cost-effective tool electrocardiogram (ECG) becomes interesting research considering smart healthcare applications. Automation, accuracy, and robustness are vital demands for an ECG-based heart disease prediction system. Deep learning brings automation to the applications like Computer-Aided Diagnosis (CAD) systems with accuracy improvement compromising robustness. We propose the novel ECG-based heart disease prediction system using the hybrid mechanism to satisfy the automation, accuracy, and robustness requirements. We design the model via the steps of pre-processing, hybrid features formation, and classification. The ECG pre-processing is aiming at suppressing the baseline and powerline interference without loss of heartbeats. We propose a hybrid mechanism that consists of handcrafted and automatic Convolutional Neural Network (CNN) lightweight features for efficient classification. The hybrid feature vector is fed to the deep learning classifier Long Term Short Memory (LSTM) sequentially to predict the disease. The simulation results show that the proposed model reduces the diagnosis errors and time compare to state-of-art methods.

A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion

2020 ◽  
Vol 63 ◽  
pp. 208-222 ◽  
Author(s):  
Farman Ali ◽  
Shaker El-Sappagh ◽  
S.M. Riazul Islam ◽  
Daehan Kwak ◽  
Amjad Ali ◽  
...  
Keyword(s):  
Heart Disease ◽  
Deep Learning ◽  
Monitoring System ◽  
Feature Fusion ◽  
Disease Prediction ◽  
Smart Healthcare ◽  
Healthcare Monitoring
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