Discriminative Neural Network for Coronary Heart Disease Detection

2020 ◽  
Vol 10 (2) ◽  
pp. 463-468
Author(s):  
Xiaoqing Gu ◽  
Yizhang Jiang ◽  
Tongguang Ni
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Feature Space ◽  
Major Type ◽  
Good Decision ◽  
The Difference ◽  
Sensitivity Specificity

Cardiovascular disease is one of the commonest diseases and main causes of death over the world. As the major type of cardiovascular diseases, correct and timely diagnosis of coronary heart disease (CHD) is very essential. Traditional back-propagation (BP) neural network aims to train a multilayer feedforward neural network which transforms data into the feature space to learn good decision boundaries. However, the performance of BP neural network tends to deteriorate when dealing with complexity medical diagnostic tasks. To improve the detection of CHD, this study proposes a discriminative neural network called DNN. DNN explores discriminative information by maximizing the difference of the compactness within each class and separability between different classes. DNN integrates the discriminative information into the framework of BP neural network, and can be easily implemented by the existing neural network software. Experimental results on Z-Alizadeh Sani dataset show that DNN achieves satisfactory performance in sensitivity, specificity, accuracy and receiver operating characteristic (ROC) curve.

scholarly journals Back Propagation Neural Network-Based Ultrasound Image for Diagnosis of Cartilage Lesions in Knee Osteoarthritis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaoming Zhao ◽  
Wei Gong ◽  
Xing Li ◽  
Weibing Yang ◽  
Dengfeng Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Knee Osteoarthritis ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Ultrasound Image ◽  
Kappa Coefficient ◽  
Control Group ◽  
Ultrasound Images ◽  
The Difference ◽  
Experimental Group

Objective. To explore the application value of ultrasound image based on back propagation (BP) neural network algorithm in knee osteoarthritis (KOA) and evaluate the application effect and value of ultrasound image technology based on the BP neural network in the diagnosis of knee osteoarthritis cartilage lesions, 98 patients who were admitted to our hospital were diagnosed with KOA and had undergone arthroscopic soft tissue examinations were randomly selected. According to whether image processing was performed, the ultrasound images of all patients were divided into two groups. The control group was image before processing, and the experimental group was image after processing optimization. The consistency of the inspection results of the ultrasound images before and after the processing with the arthroscopy results was compared. The results showed that the staging accuracy of the control group was 68.3% and that of the experimental group was 76.9%. The accuracy of staging cartilage degeneration of the experimental group was higher than that of the control group, and the difference was not remarkable ( P > 0.05 ). The kappa coefficient of the experimental group was 0.61, and that of the control group was 0.40. The kappa coefficient of the experimental group was higher than that of the control group, and the difference was significant ( P < 0.05 ). Conclusion. The inspection effect of the ultrasound image processed by the BP neural network was superior to that of the conventional ultrasound image. It reflected the good adoption prospect of neural networks in image processing.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

scholarly journals Assessment of water resource security in karst area of Guizhou Province, China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liying Liu
Keyword(s):  
Neural Network ◽  
Water Resources ◽  
Water Resource ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Water Shortage ◽  
Guizhou Province ◽  
Karst Area ◽  
Water Resource Security ◽  
Resource Security

AbstractThis paper presents the assessment of water resource security in the Guizhou karst area, China. A mean impact value and back-propagation (MIV-BP) neural network was used to understand the influencing factors. Thirty-one indices involving five aspects, the water quality subsystem, water quantity subsystem, engineering water shortage subsystem, water resource vulnerability subsystem, and water resource carrying capacity subsystem, were selected to establish an evaluation index of water resource security. In addition, a genetic algorithm and back-propagation (GA-BP) neural network was constructed to assess the water resource security of Guizhou Province from 2001 to 2015. The results show that water resource security in Guizhou was at a moderate warning level from 2001 to 2006 and a critical safety level from 2007 to 2015, except in 2011 when a moderate warning level was reached. For protection and management of water resources in a karst area, the modes of development and utilization of water resources must be thoroughly understood, along with the impact of engineering water shortage. These results are a meaningful contribution to regional ecological restoration and socio-economic development and can promote better practices for future planning.

Study on the Generalized Holo-Factors Mathematical Model of Dimension-Error and Shape-Error for Sheet Metal in Stamping Based on the Back Propagation (BP) Neural Network

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Lizhi Gu ◽  
Tianqing Zheng
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Sheet Metal ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Shape Error ◽  
Back Propagation Algorithm ◽  
Minor Strain ◽  
Major Strain ◽  
Forming Precision

Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.

Abstract P276: Differential Association of D-dimer with Stroke and Coronary Heart Disease: The REasons for Geographic and Racial Differences in Stroke (REGARDS) Study

Circulation ◽  
2013 ◽  
Vol 127 (suppl_12) ◽  
Author(s):  
Neil A Zakai ◽  
George Howard ◽  
Leslie A McClure ◽  
Suzanne E Judd ◽  
Brett M Kissela ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Vascular Disease ◽  
Racial Differences ◽  
Statistical Significance ◽  
Female Gender ◽  
Cox Models ◽  
The Difference ◽  
Chd Risk Factors ◽  
D Dimer

Introduction: D-dimer, a marker of coagulation activation, has higher levels in blacks than whites and has been variably associated with stroke and coronary heart disease (CHD). Methods: REGARDS recruited 30,239 participants in their homes across the continental US between 2003-07; by design 55% were female, 41% black, and 56% lived in the southeast. In a case-cohort study, D-dimer was measured in 646 participants with incident stroke, 515 with incident CHD, and 1104 in a cohort random sample. D-dimer was log transformed and modeled per 1-unit increase. Cox models were used to determine the HR for vascular disease for D-dimer and the difference in HR (95% CI) by race and vascular disease calculated by bootstrapping with 1000 replicate samples and using the 2.5 and 97.5 percentiles of the distribution (see Table for model variables). Results: Median D-dimer was higher in blacks (0.45 mcg/mL; IQR 0.26, 0.85) than whites (0.38 mcg/mL; IQR 0.23, 0.69); p <0.001. D-dimer was higher with increasing age, female gender, diabetes, hypertension and prebaseline cardiovascular disease (all p <0.05). The table shows the HR of stroke and CHD by baseline D-dimer. In minimally-adjusted models, D-dimer was associated with both stroke and CHD. Accounting for Framingham stroke and CHD risk factors, D-dimer remained associated with CHD (HR 1.45; 95% CI 1.18, 1.79), but was marginally associated with stroke (HR 1.20; 95% CI 0.99, 1.45). The difference in the HR of D-dimer between CHD and stroke was 0.22 in the basic model and 0.25 in the Framingham model, but this difference was of marginal statistical significance (Table). There was no difference in the HRs for stroke or CHD for D-dimer in blacks compared to whites (Table). Discussion: The association of D-dimer with stroke appeared smaller than for CHD with similar associations by race. Findings suggest that hemostasis activation may play a greater role in pathogenesis of CHD than stroke. Further study is needed to confirm these findings and evaluate the association of D-dimer with different stroke subtypes.

Effect of Tobacco on Lipoprotein Profile: A Comparative study among Smokers and Chewers

2021 ◽  
pp. 4973-4976
Author(s):  
Roshan Kumar Jha ◽  
Ranjit S. Ambad ◽  
Priya Koundal ◽  
Akansha Singh
Keyword(s):  
Risk Factors ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Total Cholesterol ◽  
Sample Processing ◽  
Exclusion Criteria ◽  
Future Studies ◽  
Area Of Interest ◽  
Significant Difference ◽  
The Difference

It has been proved that tobacco is one of the cholesterol dependent risk factors pathogenically, and in addition with other risk factors it may lead to coronary heart disease. Thus, a strong interaction exists between hypercholesterolemia and tobacco ingesting in the genesis of coronary heart disease. The aim of this study was to study the effect of tobacco smoking and chewing and compare its effect on lipoproteins. 60 subjects were included in the study, and were grouped into 3 three groups, tobacco smokers, tobacco chewers and tobacco non-abusers. Each group comprises 20 participants: selected on the basis of inclusion and exclusion criteria. Proper sampling and sample processing methods were employed to evaluate lipid profile. Total cholesterol and triglycerides levels were increased in smokers in comparison to non-smokers/non-chewers, and the differences were significant p<0.0001. HDL level was decreased in smokers as compared to non-smokers/non-chewers and the difference was statistically significant p<0.0001. Total cholesterol and LDL levels were increased in smokers in comparison to chewers. HDL level was decreased in chewers as compared to chewers. There was no significant association in any of the parameters. Present study observed increased and significant p<0.0001 differences in levels of total cholesterol and triglycerides while, HDL levels were decreased significantly p<0.0001, and also observed there was no significant difference among tobacco smokers and chewers. This may be a new area of interest for future studies.

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

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