Recognition Patterns Construction of Coronary Heart Disease Patients with Qi Deficiency Syndrome Based on Artificial Neural Network

Coronary heart disease (CHD), called “thoracic obstruction” in TCM, is one of the most important types of heart disease for its high incidence and mortality. The methods of syndrome studies in TCM can not be completely in accordance with these of modern medicine because of the complexity itself. In this paper, we investigated the ability of Artificial Neural Networks (ANNs) to predict CHD patients with or without qi deficiency syndrome. Predictions with Multilayer Perceptron Neural Network (MPLNN, one type of the ANNS), we obtained recognition patterns made up of eight biological parameters. The accuracy of this recognition pattern was 82.2%, and the accuracy of validation pattern was 80.0%.

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Recognition Patterns Construction of Coronary Heart Disease Patients with Qi Stagnation Syndrome Based on Decision Tree

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.475-476.1025 ◽

2013 ◽

Vol 475-476 ◽

pp. 1025-1031

Author(s):

Qi Shi ◽

You Lin Li ◽

Hui Hui Zhao ◽

Jian Xin Chen ◽

Xin Qiu Wang ◽

...

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Decision Tree ◽

Sensitivity And Specificity ◽

Biological Indicators ◽

Modern Medicine ◽

Biological Parameters ◽

Recognition Pattern ◽

High Incidence ◽

Qi Stagnation

Coronary heart disease (CHD), called Thoracic Obstruction in TCM, is one of the most important types of heart disease for its high incidence and high mortality. The methods of syndrome studies in TCM can not be completely in accordance with that of modern medicine because of the complexity itself. In this paper, we decide to investigate the ability of Decision Tree to predict CHD patients with or without qi stagnation syndrome. Predictions with CHAID Decision Tree (one type of the Decision Trees), we obtained recognition patterns made up of seven biological parameters. The accuracy of this diagnosis pattern was 80.7%, the sensitivity and specificity could reach 72.1% and 83.0%. The ADTree recognition pattern include six biological indicators. The accuracy of this diagnosis pattern was 94.6%, the sensitivity and specificity could reach 100% and 94.3%.

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The Analysis of Performace Model Tiered Artificial Neural Network for Assessment of Coronary Heart Disease

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i4.pp2183-2191 ◽

2017 ◽

Vol 7 (4) ◽

pp. 2183

Author(s):

Wiharto Wiharto ◽

Harianto Herianto ◽

Hari Kusnanto

Keyword(s):

Neural Network ◽

Coronary Heart Disease ◽

Artificial Neural Network ◽

Heart Disease ◽

Assessment Model ◽

Model Assessment ◽

Marquardt Algorithm ◽

One Step ◽

Artificial Neural ◽

Artificial Neural Network Ann

<p>The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, the tiered approach. This study aims to analyze the performance of a tiered model assessment. The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The study is divided into the terms of the stages in the examination procedure. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The performance evaluation may indicate that the top level provides performance improvement and or reinforce the previous level. </p>

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An Efficient Artificial Neural Network for Coronary Heart Disease Prediction

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39559 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1474-1483

Author(s):

Priyam Vinay Sheta

Keyword(s):

Neural Network ◽

Coronary Heart Disease ◽

Artificial Neural Network ◽

Logistic Regression ◽

Heart Disease ◽

Early Stage ◽

Large Population ◽

Input Feature ◽

The Neural Network ◽

Artificial Neural

Abstract: Coronary heart disease is rapidly increasing over these days also with a significant number of deaths. A large population around the world is suffering from the disease. When surveys were carried out of the death rate and the number of people suffering from the coronary heart disease, it was understood that how important is the diagnosis of this disease at an early stage. The old way for detecting the disease was not found effective. This paper suggests a different method and technology to detect the disease and the proposed method is more effective than the old traditional methods. In this paper, an artificial neural network that predicts the coronary heart disease is used with 14 features as the input. Feature selection, data preprocessing, and removing irrelevant data was done before training the neural network. The backpropagation algorithm was used for making the neural network learn the features. The output of data was basically binary but the neural network was trained to give the output as a probability between 0 and 1. Two algorithms were proposed for this prediction named Logistic Regression and Artificial Neural Network but the later was selected because of the accuracy of 94%. The accuracy of Logistic Regression was 87%.

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Artificial neural network versus multiple logistic function to predict 25-year coronary heart disease mortality in the Seven Countries Study

European Journal of Cardiovascular Prevention & Rehabilitation ◽

10.1097/hjr.0b013e32832d49e1 ◽

2009 ◽

Vol 16 (5) ◽

pp. 583-591 ◽

Cited By ~ 18

Author(s):

Paolo Emilio Puddu ◽

Alessandro Menotti

Keyword(s):

Neural Network ◽

Coronary Heart Disease ◽

Artificial Neural Network ◽

Heart Disease ◽

Coronary Heart Disease Mortality ◽

Logistic Function ◽

Disease Mortality ◽

Seven Countries Study ◽

Artificial Neural

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Study on TCM Syndrome Identification Modes of Coronary Heart Disease Based on Data Mining

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2012/697028 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 22

Author(s):

Qi Shi ◽

Huihui Zhao ◽

Jianxin Chen ◽

Xueling Ma ◽

Yi Yang ◽

...

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

System Biology ◽

Rapid Development ◽

External Validation ◽

Diagnostic Methods ◽

Syndrome Differentiation ◽

Biological Parameters ◽

High Incidence ◽

Tcm Syndrome

Coronary heart disease (CHD) is one of the most important types of heart disease because of its high incidence and high mortality. TCM has played an important role in the treatment of CHD. Syndrome differentiation based on information from traditional four diagnostic methods has met challenges and questions with the rapid development and wide application of system biology. In this paper, methods of complex network and CHAID decision tree were applied to identify the TCM core syndromes of patients with CHD, and to establish TCM syndrome identification modes of CHD based on biological parameters. At the same time, external validation modes were also constructed to confirm the identification modes.

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Discovery of Diagnosis Pattern of Coronary Heart Disease with Qi Deficiency Syndrome by theT-Test-Based Adaboost Algorithm

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2011/408650 ◽

2011 ◽

Vol 2011 ◽

pp. 1-7 ◽

Cited By ~ 11

Author(s):

Huihui Zhao ◽

Jianxin Chen ◽

Na Hou ◽

Peng Zhang ◽

Yong Wang ◽

...

Keyword(s):

Data Mining ◽

Coronary Heart Disease ◽

Heart Disease ◽

Cross Validation ◽

Deficiency Syndrome ◽

Biological Parameters ◽

Mining Method ◽

Adaboost Algorithm ◽

Data Mining Method ◽

Validation Technique

Coronary heart disease (CHD) is still the leading cause of death for adults worldwide. Traditional Chinese medicine (TCM) has a history of 1000 years fighting against the disease and provides a complementary and alternative treatment to it. Syndrome is the core of TCM diagnosis and it is traditionally diagnosed based on macroscopic symptoms as well as tongue and pulse recognitions of patients. Establishment of the diagnosis method in the microcosmic level is an urgent and major problem in TCM. The aim of this study was to establish characteristic diagnosis pattern for CHD with Qi deficiency syndrome (QDS). Thirty-four biological parameters were detected in 52 patients having unstable angina (UA) with or without QDS. Then, we presented a novel data mining method,t-test-based Adaboost algorithm, to establish highest prediction accuracy with the least number of biological parameters for UA with QDS. We gained a pattern composed of five biological parameters that distinguishes UA with QDS patients from non-QDS patients. The diagnosis accuracy of the patterns could reach 84.5% based on a 3-fold cross validation technique. Moreover, we included 85 UA cases collected from hospitals located in the north and south of China to further verify the association between the pattern and QDS. The classification accuracy is 83.5%, which keeps consistent with the accuracy obtained by the cross-validation technique. The association between a symptom and the five biological parameters was established by the data mining method and it reached an accuracy of ∼80%. These results showed that thet-test-based Adaboost algorithm might be a powerful technique for diagnosing syndrome in TCM in the context of CHD.

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Study on Qi Deficiency Syndrome Identification Modes of Coronary Heart Disease Based on Metabolomic Biomarkers

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2014/281829 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15 ◽

Cited By ~ 7

Author(s):

Qi Shi ◽

Huihui Zhao ◽

Jianxin Chen ◽

Youlin Li ◽

Zhongfeng Li ◽

...

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Deficiency Syndrome ◽

Healthy People ◽

Dynamic Responses ◽

Plasma Metabolites ◽

Incidence And Mortality ◽

Exogenous Factors ◽

Concentration Changes ◽

Endogenous Metabolites

Coronary heart disease (CHD) is one of the most important types of heart disease because of its high incidence and mortality. With the era of systems biology bursting into reality, the analysis of the whole biological systems whether they are cells, tissues, organs, or the whole organisms has now become the norm of biological researches. Metabolomics is the branch of science concerned with the quantitative understandings of the metabolite complement of integrated living systems and their dynamic responses to the changes of both endogenous and exogenous factors. The aim of this study is to discuss the characteristics of plasma metabolites in CHD patients and CHD Qi deficiency syndrome patients and explore the composition and concentration changes of the plasma metabolomic biomarkers. The results show that 25 characteristic metabolites related to the CHD patients comparing with the healthy people, and 4 identifiable variables had significant differences between Qi deficiency and non-Qi deficiency patients. On the basis of identifying the different plasma endogenous metabolites between CHD patients and healthy people, we further prompted the metabolic rules, pathogenesis, and biological essence in Qi deficiency syndrome patients.

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