Syndromes diagnostic model for coronary artery disease (CAD): An improved naïve Bayesian classification model based on attribute relevancy

2017 ◽  
Author(s):  
Kai Lei ◽  
Lizhu Zhang ◽  
Ying Shen ◽  
Xiaohui Huang ◽  
Jincheng Wu
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Bayesian Classification ◽  
Classification Model ◽  
Diagnostic Model ◽  
Naive Bayesian ◽  
Model Based ◽  
Naïve Bayesian ◽  
Artery Disease

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

scholarly journals Simple risk models to predict cardiovascular death in patients with stable coronary artery disease

2020 ◽  
Author(s):  
Ian Ford ◽  
Michele Robertson ◽  
Nicola Greenlaw ◽  
Christophe Bauters ◽  
Gilles Lemesle ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Risk Model ◽  
Stable Coronary Artery Disease ◽  
Cardiovascular Death ◽  
Left Ventricular ◽  
Cox Proportional Hazards Model ◽  
Clinical Variables ◽  
Model Based ◽  
Artery Disease

Abstract Aims Risk estimation is important to motivate patients to adhere to treatment and to identify those in whom additional treatments may be warranted and expensive treatments might be most cost effective. Our aim was to develop a simple risk model based on readily available risk factors for patients with stable coronary artery disease (CAD). Methods and results Models were developed in the CLARIFY registry of patients with stable CAD, first incorporating only simple clinical variables and then with the inclusion of assessments of left ventricular function, estimated glomerular filtration rate, and haemoglobin levels. The outcome of cardiovascular death over ∼5 years was analysed using a Cox proportional hazards model. Calibration of the models was assessed in an external study, the CORONOR registry of patients with stable coronary disease. We provide formulae for calculation of the risk score and simple integer points-based versions of the scores with associated look-up risk tables. Only the models based on simple clinical variables provided both good c-statistics (0.74 in CLARIFY and 0.80 or over in CORONOR), with no lack of calibration in the external dataset. Conclusion Our preferred model based on 10 readily available variables [age, diabetes, smoking, heart failure (HF) symptom status and histories of atrial fibrillation or flutter, myocardial infarction, peripheral arterial disease, stroke, percutaneous coronary intervention, and hospitalization for HF] had good discriminatory power and fitted well in an external dataset. Study registration The CLARIFY registry is registered in the ISRCTN registry of clinical trials (ISRCTN43070564).

scholarly journals Bioinformatics analysis identifies potential diagnostic signatures for coronary artery disease

2020 ◽  
Vol 48 (12) ◽  
pp. 030006052097985
Author(s):  
Dong Zhang ◽  
Liying Guan ◽  
Xiaoming Li
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Differential Expression ◽  
Protein Interactions ◽  
Diagnostic Model ◽  
Ppi Network ◽  
Kegg Pathways ◽  
Clinical Diagnoses ◽  
Key Genes ◽  
Artery Disease

Background Coronary artery disease (CAD) is the leading cause of mortality worldwide. We aimed to screen out potential gene signatures and construct a diagnostic model for CAD. Method We downloaded two mRNA profiles, GSE66360 and GSE60993, and performed analyses of differential expression, gene ontology terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The STRING database was used to identify protein–protein interactions (PPI). PPI network visualization and screening out of key genes were performed using Cytoscape software. Finally, a diagnostic model was constructed. Results A total of 2127 differentially expressed genes (DEGs) were identified in GSE66360, and 527 DEGs in GSE60993. Of the 153 DEGs from both datasets that showed differential expression between CAD patients and controls, 471 biological process terms, 35 cellular component terms, 17 molecular function terms, and 49 KEGG pathways were significantly enriched. The top 20 key genes in the PPI network were identified, and a diagnostic model constructed from five optimal genes that could efficiently separate CAD patients from controls. Conclusion We identified several potential biomarkers for CAD and built a logistic regression model that will provide a valuable reference for future clinical diagnoses and guide therapeutic strategies.

scholarly journals Dysbiosis signatures of gut microbiota in coronary artery disease

2018 ◽  
Vol 50 (10) ◽  
pp. 893-903 ◽  
Author(s):  
Qi Zhu ◽  
Renyuan Gao ◽  
Yi Zhang ◽  
Dengdeng Pan ◽  
Yefei Zhu ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Gut Microbiota ◽  
Diagnostic Model ◽  
Phosphotransferase System ◽  
Microbial Network ◽  
Healthy Control ◽  
Artery Disease ◽  
Novel Strategy ◽  
And Function

Gut microbiota dysbiosis has been considered to be an important risk factor that contributes to coronary artery disease (CAD), but limited evidence exists about the involvement of gut microbiota in the disease. Our study aimed to characterize the dysbiosis signatures of gut microbiota in coronary artery disease. The gut microbiota represented in stool samples were collected from 70 patients with coronary artery disease and 98 healthy controls. 16S rRNA sequencing was applied, and bioinformatics methods were used to decipher taxon signatures and function alteration, as well as the microbial network and diagnostic model of gut microbiota in coronary artery disease. Gut microbiota showed decreased diversity and richness in patients with coronary artery disease. The composition of the microbial community changed; Escherichia-Shigella [false discovery rate (FDR = 7.5*10−5] and Enterococcus (FDR = 2.08*10−7) were significant enriched, while Faecalibacterium (FDR = 6.19*10−10), Subdoligranulum (FDR = 1.63*10−6), Roseburia (FDR = 1.95*10−9), and Eubacterium rectale (FDR = 2.35*10−4) were significant depleted in the CAD group. Consistent with the taxon changes, functions such as amino acid metabolism, phosphotransferase system, propanoate metabolism, lipopolysaccharide biosynthesis, and protein and tryptophan metabolism were found to be enhanced in CAD patients. The microbial network revealed that Faecalibacterium and Escherichia-Shigella were the microbiotas that dominated in the healthy control and CAD groups, respectively. The microbial diagnostic model based on random forest also showed probability in identifying those who suffered from CAD. Our study successfully identifies the dysbiosis signature, dysfunctions, and comprehensive networks of gut microbiota in CAD patients. Thus, modulation targeting the gut microbiota may be a novel strategy for CAD treatment.

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