Risk Prediction of Heart Disease Based on Swarm Optimized Neural Network

2014 ◽  
pp. 707-714 ◽  
Author(s):  
R. Chitra ◽  
V. Seenivasagam
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Risk Prediction

scholarly journals Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Jae Kwon Kim ◽  
Sanggil Kang
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Correlation Analysis ◽  
Risk Prediction ◽  
Roc Curve ◽  
Chd Risk ◽  
Proposed Model ◽  
Feature Correlation ◽  
Clinical Experiments

Background. Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA) using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC) curve of the proposed model (0.749 ± 0.010) was larger than the Framingham risk score (FRS) (0.393 ± 0.010). Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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 Accuracy of risk prediction scores in pregnant women with congenital heart disease

2019 ◽  
Vol 14 (3) ◽  
pp. 470-478 ◽  
Author(s):  
Yuli Y. Kim ◽  
Leah A. Goldberg ◽  
Katherine Awh ◽  
Tanmay Bhamare ◽  
David Drajpuch ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Pregnant Women ◽  
Risk Prediction ◽  
Congenital Heart
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