A Model for Heart Disease Prediction Using Feature Selection with Deep Learning

2021 ◽  
pp. 151-168
Author(s):  
Vaishali Baviskar ◽  
Madhushi Verma ◽  
Pradeep Chatterjee
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Deep Learning ◽  
Disease Prediction

scholarly journals A Review of Feature Selection and Classification Approaches for Heart Disease Prediction

2021 ◽  
Vol 4 (3) ◽  
pp. 75
Author(s):  
Fathania Firwan Firdaus ◽  
Hanung Adi Nugroho ◽  
Indah Soesanti
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Heart Disease ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Hybrid Approach ◽  
Filter Method ◽  
Future Research ◽  
Computer Assisted ◽  
Disease Prediction

Cardiovascular disease has been the number one illness to cause death in the world for years. As information technology develops, many researchers have conducted studies on a computer-assisted diagnosis for heart disease. Predicting heart disease using a computer-assisted system can reduce time and costs. Feature selection can be used to choose the most relevant variables for heart disease. It includes filter, wrapper, embedded, and hybrid. The filter method excels in computation speed. The wrapper and embedded methods consider feature dependencies and interact with classifiers. The hybrid method takes advantage of several methods. Classification is a data mining technique to predict heart disease. It includes traditional machine learning, ensemble learning, hybrid, and deep learning. Traditional machine learning uses a specific algorithm. The ensemble learning combines the predictions of multiple classifiers to improve the performance of a single classifier. The hybrid approach combines some techniques and takes advantage of each method. Deep learning does not require a predetermined feature engineering. This research provides an overview of feature selection and classification methods for the prediction of heart disease in the last ten years. Thus, it can be used as a reference in choosing a method for heart disease prediction for future research.

scholarly journals Classification models for heart disease prediction using feature selection and PCA

2020 ◽  
Vol 19 ◽  
pp. 100330 ◽  
Author(s):  
Anna Karen Gárate-Escamila ◽  
Amir Hajjam El Hassani ◽  
Emmanuel Andrès
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Disease Prediction ◽  
Classification Models

scholarly journals Heart disease prediction using SVM based neuro-fuzzy technique in the cloud computing

2018 ◽  
Vol 7 (2.20) ◽  
pp. 153
Author(s):  
Dr M. Sadish Sendil
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Feature Selection ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Disease Prediction ◽  
Specific Level ◽  
Huge Data ◽  
Neuro Fuzzy ◽  
Heart Disease Diagnosis

Cloud computing is a technique for conveying on information development benefits in resources are recovered from the Internet through online based device and applications, as opposed to a speedy association with a server. Cloud has numerous applications in the meadows of education, social networking, and medicine. But the benefit of the cloud for medical reasons is seamless, specifically an account of the huge data generated by the healthcare industry. Heart disease diagnosis determination strategy is essential and significant issue for the patient's wellbeing. Furthermore, it will help to decrease infection to a more specific level. Computer-aided decision support method performs a vital task in medical line. Data mining gives the system and innovation to change these heaps of data into effective information for decision-making. When applying data mining techniques it carries shorter time for the prediction of the disease with more exactness. The hybrid work of preprocessing, feature selection using SVM and SVM based Neuro-Fuzzy data mining strategies utilizing as a part of the determination of the heart disease is incredibly impressive. The framework is to build up a technique for arranging for heart level of the patient relies upon highlight information utilizing Neuro-Fuzzy surmising system. The experiment is done with two different analysis that is one with preprocessed data alone and applied SVM based Neuro Fuzzy Technique and the second one is accomplished with feature selection done data and applied SVM based Neuro Fuzzy Technique. The results prove that the system result of the first one gives 92% accuracy in the heart disease prediction. The second one is giving 95.11% accuracy in the heart disease prediction.  

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.

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