scholarly journals An Efficient Convolutional Neural Network for Coronary Heart Disease Prediction

2019 ◽  
Author(s):  
Aniruddha Dutta ◽  
Tamal Batabyal ◽  
Meheli Basu ◽  
Scott Acton
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Convolutional Neural Network ◽  
Disease Prediction

scholarly journals An efficient convolutional neural network for coronary heart disease prediction

2020 ◽  
Vol 159 ◽  
pp. 113408 ◽  
Author(s):  
Aniruddha Dutta ◽  
Tamal Batabyal ◽  
Meheli Basu ◽  
Scott T. Acton
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Convolutional Neural Network ◽  
Disease Prediction

scholarly journals An Improved Coronary Heart Disease Predictive System Using Random Forest

2021 ◽  
pp. 17-27
Author(s):  
Abdulraheem Abdul ◽  
Rafiu M. Isiaka ◽  
Ronke S. Babatunde ◽  
Jumoke F. Ajao
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Feature Selection ◽  
Heart Disease ◽  
Random Forest ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbor

Aims: This work aim is to develop an enhanced predictive system for Coronary Heart Disease (CHD). Study Design: Synthetic Minority Oversampling Technique and Random Forest. Methodology: The Framingham heart disease dataset was used, which was collected from a study in Framingham, Massachusetts, the data was cleaned, normalized, rebalanced. Classifiers such as random forest, artificial neural network, naïve bayes, logistic regression, k-nearest neighbor and support vector machine were used for classification. Results: Random Forest outperformed other classifiers with an accuracy of 98%, a sensitivity of 99% and a precision of 95.8%. Feature selection was employed for better classification, but  no significant improvement was recorded on the performance of the classifier with feature selection. Train test split also performed better that cross validation. Conclusion: Random Forest is recommended for research in Coronary Heart Disease prediction domain.

An intelligent heart disease prediction system based on swarm-artificial neural network

2021 ◽  
Author(s):  
Sudarshan Nandy ◽  
Mainak Adhikari ◽  
Venki Balasubramanian ◽  
Varun G. Menon ◽  
Xingwang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Disease Prediction ◽  
Prediction System ◽  
Artificial Neural

scholarly journals Deep Convolutional Neural Network Feed Forward and Back Prop a gation (DCNN F BP) Algorithm f or Predicting Heart Disease u sing Internet o f Things

2021 ◽  
Vol 11 (1) ◽  
pp. 283-287
Author(s):  
Saranya N ◽  
◽  
Kavi Priya S ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Heart Disease ◽  
Convolutional Neural Network ◽  
Back Propagation ◽  
High Volume ◽  
Deep Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Batch Size ◽  
Feed Forward

In recent years, due to the increasing amounts of data gathered from the medical area, the Internet of Things are majorly developed. But the data gathered are of high volume, velocity, and variety. In the proposed work the heart disease is predicted using wearable devices. To analyze the data efficiently and effectively, Deep Canonical Neural Network Feed-Forward and Back Propagation (DCNN-FBP) algorithm is used. The data are gathered from wearable gadgets and preprocessed by employing normalization. The processed features are analyzed using a deep convolutional neural network. The DCNN-FBP algorithm is exercised by applying forward and backward propagation algorithm. Batch size, epochs, learning rate, activation function, and optimizer are the parameters used in DCNN-FBP. The datasets are taken from the UCI machine learning repository. The performance measures such as accuracy, specificity, sensitivity, and precision are used to validate the performance. From the results, the model attains 89% accuracy. Finally, the outcomes are juxtaposed with the traditional machine learning algorithms to illustrate that the DCNN-FBP model attained higher accuracy.

Sign in / Sign up

Export Citation Format

Share Document