Coronary Artery Disease Dataset Research Articles

Abstract 13799: A Systems Approach to Target Discovery Identifies the Novel Role of lncRNA-SPANXA2-OT1 in Macrophage Chemotaxis

Background: Coronary artery disease (CAD) remains the leading cause of mortality worldwide. Macrophages play a crucial role in recruiting immune cells and regulating the inflammatory milieu through the release of a diverse array of cytokines and chemokines in CAD. Long noncoding RNAs (lncRNAs) interact with DNA, RNA, miRNA, and proteins which makes them attractive therapeutic targets. We hypothesized that the lncRNA, SPANXA2-OT1 , regulates macrophage chemotaxis in CAD. Methods and Results: Gene-expression meta-analysis and weighted gene co-expression network analysis (WGCNA) of human CAD datasets identified 26 lncRNA-mRNA co-expression modules (Fig A). Network prioritization of top co-expression modules identified the SPANXA2-OT1 as a potential key candidate. The conservation analysis revealed that SPANXA2-OT1 is conserved only in primates. We validated the candidate coding-noncoding RNA regulatory triad in primary macrophages derived from healthy human PBMCs (n= 3-10). Loss-of-function experiments using antisense oligonucleotide (ASO) against SPANXA2-OT1 demonstrated decreased monocyte chemotaxis signature (Figure B and D), as recorded by unbiased global proteomics and RNA expression (IL8, CCL19 and CCL23) data. The expression of S PANXA2-OT1 was stimulated by IL1β (Fig C) and RNA in situ hybridization demonstrated it to be highly expressed in cytoplasm (Fig E) of macrophages. Luciferase assay established that SPANXA2-OT1 binds to miR-338 through its miRNA response elements (Fig F). Gain-of-function ( miR-338 mimic) and loss-of-function ( SPANXA2-OT1 ASO) experiments revealed that SPANXA2-OT1 : miR-338 axis regulates the expression of monocyte chemotactic genes (e.g., IL-8 ) that may contribute to the pathophysiology of CAD (Fig G and H). Conclusion: Our results indicate that the lncRNA SPANXA2-OT1 regulates chemokine signature in macrophages and may provide a molecular basis for the identification of novel therapeutic targets for CAD.

A precise coronary artery disease prediction using Boosted C5.0 decision tree model

<p>In coronary artery disease, plaque builds up in the arteries that carry oxygen-rich blood to the heart. Having plaque in the arteries can constrict or impede blood flow, leading to a heart attack. Shortness of breath and soreness in the chest are common symptoms. Lifestyle modifications, medication, and potentially surgery are all options for treatment. In coronary artery disease, plaque builds up in the arteries that carry oxygen-rich blood to the heart. Having plaque in the arteries can constrict or impede blood flow, leading to a heart attack. Shortness of breath and soreness in the chest are common symptoms. Lifestyle modifications, medication, and potentially surgery are all options for treatment. This paper presents a Hybrid Boosted C5.0 model to predict coronary artery disease more precisely. A Hybrid Boosted C5.0 model is formed by combining the C5.0 decision tree and boosting methods. Boosting is a supervised machine learning method that leverages numerous inadequate models to construct a more robust and powerful model. The proposed model and some well-known existing machine learning models, i.e., decision tree, AdaBoost, and random forest, were implemented using an online coronary artery disease dataset of 6611 patients and compared based on various performance measuring parameters. Experimental analysis shows that the proposed model achieved an accuracy of 91.62% at training and 81.33% at the testing phase. The AUC value achieved in the training and testing phase is 0.957 and 0.88, respectively. The Gini value achieved in the training and testing phase is 0.914 and 0.759, respectively, far better than the proposed method.</p>

Prediction of Coronary Heart Disease using Deep Learning Models

Abstract: More over 80% of deaths from heart disease, including those in Nigeria, are caused by coronary artery disease (CAD), the most prevalent type. The victims were mostly less than 70 years old. More than 17 million people died in 2015 from CVDrelated causes, accounting for more than 30% of all deaths worldwide. It develops over time and progresses through several stages. The stages of CAD include Fatty Streaks, Mild atherosclerosis, Moderate atherosclerosis, Sever atherosclerosis. In this paper, a diagnostic CAD dataset that was collected from the Kaggle website was used to construct a machine learning predictive model for CAD. The dataset was used to create prediction models using machine learning techniques like Naive Bayes, Support Vector Machine, Random Forest, and Gradient Boosting and DNN are applied to compare the results and analysis of the CAD Disease. The models' accuracy, precision, recall, and f1score scores were assessed using performance evaluation methodologies in order to determine the CAD that would have or had already occurred based on a person’s physical condition and data from medical records. Result shows that compared to ML algothims and DL technique, DNN gives more accuracy in less time for the prediction. The prediction accuracy obtained by DNN algorithm is 93.56% and the prediction accuracy obtained by SVM is 83.34%.

ALEC: Active learning with ensemble of classifiers for clinical diagnosis of coronary artery disease

Invasive angiography is the reference standard for coronary artery disease (CAD) diagnosis but is expensive and associated with certain risks. Machine learning (ML) using clinical and noninvasive imaging parameters can be used for CAD diagnosis to avoid the side effects and cost of angiography. However, ML methods require labeled samples for efficient training. The labeled data scarcity and high labeling costs can be mitigated by active learning. This is achieved through selective query of challenging samples for labeling. To the best of our knowledge, active learning has not been used for CAD diagnosis yet. An Active Learning with Ensemble of Classifiers (ALEC) method is proposed for CAD diagnosis, consisting of four classifiers. Three of these classifiers determine whether a patient's three main coronary arteries are stenotic or not. The fourth classifier predicts whether the patient has CAD or not. ALEC is first trained using labeled samples. For each unlabeled sample, if the outputs of the classifiers are consistent, the sample along with its predicted label is added to the pool of labeled samples. Inconsistent samples are manually labeled by medical experts before being added to the pool. The training is performed once more using the samples labeled so far. The interleaved phases of labeling and training are repeated until all samples are labeled. Compared with 19 other active learning algorithms, ALEC combined with a support vector machine classifier attained superior performance with 97.01% accuracy. Our method is justified mathematically as well. We also comprehensively analyze the CAD dataset used in this paper. As part of dataset analysis, features pairwise correlation is computed. The top 15 features contributing to CAD and stenosis of the three main coronary arteries are determined. The relationship between stenosis of the main arteries is presented using conditional probabilities. The effect of considering the number of stenotic arteries on sample discrimination is investigated. The discrimination power over dataset samples is visualized, assuming each of the three main coronary arteries as a sample label and considering the two remaining arteries as sample features.

Sex-Based Performance Disparities in Machine Learning Algorithms for Cardiac Disease Prediction: Exploratory Study.

The presence of bias in artificial intelligence has garnered increased attention, with inequities in algorithmic performance being exposed across the fields of criminal justice, education, and welfare services. In health care, the inequitable performance of algorithms across demographic groups may widen health inequalities. Here, we identify and characterize bias in cardiology algorithms, looking specifically at algorithms used in the management of heart failure. Stage 1 involved a literature search of PubMed and Web of Science for key terms relating to cardiac machine learning (ML) algorithms. Papers that built ML models to predict cardiac disease were evaluated for their focus on demographic bias in model performance, and open-source data sets were retained for our investigation. Two open-source data sets were identified: (1) the University of California Irvine Heart Failure data set and (2) the University of California Irvine Coronary Artery Disease data set. We reproduced existing algorithms that have been reported for these data sets, tested them for sex biases in algorithm performance, and assessed a range of remediation techniques for their efficacy in reducing inequities. Particular attention was paid to the false negative rate (FNR), due to the clinical significance of underdiagnosis and missed opportunities for treatment. In stage 1, our literature search returned 127 papers, with 60 meeting the criteria for a full review and only 3 papers highlighting sex differences in algorithm performance. In the papers that reported sex, there was a consistent underrepresentation of female patients in the data sets. No papers investigated racial or ethnic differences. In stage 2, we reproduced algorithms reported in the literature, achieving mean accuracies of 84.24% (SD 3.51%) for data set 1 and 85.72% (SD 1.75%) for data set 2 (random forest models). For data set 1, the FNR was significantly higher for female patients in 13 out of 16 experiments, meeting the threshold of statistical significance (-17.81% to -3.37%; P<.05). A smaller disparity in the false positive rate was significant for male patients in 13 out of 16 experiments (-0.48% to +9.77%; P<.05). We observed an overprediction of disease for male patients (higher false positive rate) and an underprediction of disease for female patients (higher FNR). Sex differences in feature importance suggest that feature selection needs to be demographically tailored. Our research exposes a significant gap in cardiac ML research, highlighting that the underperformance of algorithms for female patients has been overlooked in the published literature. Our study quantifies sex disparities in algorithmic performance and explores several sources of bias. We found an underrepresentation of female patients in the data sets used to train algorithms, identified sex biases in model error rates, and demonstrated that a series of remediation techniques were unable to address the inequities present.

A Machine Learning Model for Detection of Coronary Artery Disease Using Noninvasive Clinical Parameters.

Background and Objective: Coronary artery disease (CAD) is one of the most prevalent causes of death worldwide. The early diagnosis and timely medical care of cardiovascular patients can greatly prevent death and reduce the cost of treatments associated with CAD. In this study, we attempt to prepare a new model for early CAD diagnosis. The proposed model can diagnose CAD based on clinical data and without the use of an invasive procedure. Methods: In this paper, machine-learning (ML) techniques were used for the early detection of CAD, which were applied to a CAD dataset known as Z-Alizadeh Sani. Since this dataset has 54 features, the Pearson correlation feature selection method was conducted to identify the most effective features. Then, six machine learning techniques including decision tree, deep learning, logistic regression, random forest, support vector machine (SVM), and Xgboost were employed based on a semi-random-partitioning framework. Result: Applying Pearson feature selection to the dataset demonstrated that only eight features were the most effective for CAD diagnosis. The results of running the six machine-learning models on the selected features showed that logistic regression and SVM had the same performance with 95.45% accuracy, 95.91% sensitivity, 91.66% specificity, and a 96.90% F1 score. In addition, the ROC curve indicates a similar result regarding the AUC (0.98). Conclusions: Prediction is an important component of medical decision support systems. The results of the present study showed that feature selection has a high impact on machine-learning performance and, regardless of the evaluation metrics of the machine-learning models, determining the effective features is very important. However, SVM and Logistic Regression were designated as the best models according to our selected features.

Ensemble feature selection and classification methods for machine learning-based coronary artery disease diagnosis

Coronary artery disease (CAD) is a condition in which the heart is not fed sufficiently as a result of the accumulation of fatty matter. As reported by the World Health Organization, around 32% of the total deaths in the world are caused by CAD, and it is estimated that approximately 23.6 million people will die from this disease in 2030. CAD develops over time, and the diagnosis of this disease is difficult until a blockage or a heart attack occurs. In order to bypass the side effects and high costs of the current methods, researchers have proposed to diagnose CADs with computer-aided systems, which analyze some physical and biochemical values at a lower cost. In this study, for the CAD diagnosis, (i) seven different computational feature selection (FS) methods, one domain knowledge-based FS method, and different classification algorithms have been evaluated; (ii) an exhaustive ensemble FS method and a probabilistic ensemble FS method have been proposed. The proposed approach is tested on three publicly available CAD data sets using six different classification algorithms and four different variants of voting algorithms. The performance metrics have been comparatively evaluated with numerous combinations of classifiers and FS methods. The multi-layer perceptron classifier obtained satisfactory results on three data sets. Performance evaluations show that the proposed approach resulted in 91.78%, 85.55%, and 85.47% accuracy for the Z-Alizadeh Sani, Statlog, and Cleveland data sets, respectively.

Identification of molecular subtypes of coronary artery disease based on ferroptosis- and necroptosis-related genes.

Aim: Coronary artery disease (CAD) is a heterogeneous disorder with high morbidity, mortality, and healthcare costs, representing a major burden on public health. Here, we aimed to improve our understanding of the genetic drivers of ferroptosis and necroptosis and the clustering of gene expression in CAD in order to develop novel personalized therapies to slow disease progression. Methods: CAD datasets were obtained from the Gene Expression Omnibus. The identification of ferroptosis- and necroptosis-related differentially expressed genes (DEGs) and the consensus clustering method including the classification algorithm used km and distance used spearman were performed to differentiate individuals with CAD into two clusters (cluster A and cluster B) based expression matrix of DEGs. Next, we identified four subgroup-specific genes of significant difference between cluster A and B and again divided individuals with CAD into gene cluster A and gene cluster B with same methods. Additionally, we compared differences in clinical information between the subtypes separately. Finally, principal component analysis algorithms were constructed to calculate the cluster-specific gene score for each sample for quantification of the two clusters. Results: In total, 25 ferroptosis- and necroptosis-related DEGs were screened. The genes in cluster A were mostly related to the neutrophil pathway, whereas those in cluster B were mostly related to the B-cell receptor signaling pathway. Moreover, the subgroup-specific gene scores and CAD indices were higher in cluster A and gene cluster A than in cluster B and gene cluster B. We also identified and validated two genes showing upregulation between clusters A and B in a validation dataset. Conclusion: High expression of CBS and TLR4 was related to more severe disease in patients with CAD, whereas LONP1 and HSPB1 expression was associated with delayed CAD progression. The identification of genetic subgroups of patients with CAD may improve clinician knowledge of disease pathogenesis and facilitate the development of methods for disease diagnosis, classification, and prognosis.

Cholesterol not particle concentration mediates the atherogenic risk conferred by apolipoprotein B particles: a Mendelian randomization analysis.

The causal contribution of apolipoprotein B (apoB) particles to coronary artery disease (CAD) is established. We examined whether this atherogenic contribution is better reflected by non-high-density lipoprotein cholesterol (non-HDL-C) or apoB particle concentration. We performed Mendelian randomization (MR) analysis using 235 variants as genetic instruments; testing the relationship between their effects on the exposures, non-HDL-C and apoB, and on the outcome CAD using weighted regression. Variant effect estimates on the exposures came from the UK Biobank (N = 376 336) and on the outcome from a meta-analysis of five CAD datasets (187 451 cases and 793 315 controls). Subsequently, we carried out sensitivity and replication analyses.In univariate MR analysis, both exposures associated with CAD (βnon-HDL-C = 0.40, P = 2.8 × 10-48 and βapoB = 0.38, P = 1.3 × 10-44). Adding effects on non-HDL-C into a model that already included those on apoB significantly improved the genetically predicted CAD effects (P = 3.9 × 10-5), while adding apoB into the model including non-HDL-C did not (P = 0.69). Thirty-five per cent (82/235) of the variants used as genetic instruments had discordant effects on the exposures, associating with non-HDL-C/apoB ratio at P < 2.1 × 10-4 (0.05/235). Fifty-one variants associated at genome-wide significance. Many sequence variants have discordant effects on non-HDL-C and apoB. These variants allowed us to show that the causal mechanism underlying the relationship between apolipoprotein B particles and CAD is more associated with non-HDL-C than apoB particle concentration.

An efficient classification rule generation for coronary artery disease diagnosis using a novel discrete equilibrium optimizer algorithm

Many machine learning-based methods have been widely applied to Coronary Artery Disease (CAD) and are achieving high accuracy. However, they are black-box methods that are unable to explain the reasons behind the diagnosis. The trade-off between accuracy and interpretability of diagnosis models is important, especially for human disease. This work aims to propose an approach for generating rule-based models for CAD diagnosis. The classification rule generation is modeled as combinatorial optimization problem and it can be solved by means of metaheuristic algorithms. Swarm intelligence algorithms like Equilibrium Optimizer Algorithm (EOA) have demonstrated great performance in solving different optimization problems. Our present study comes up with a Novel Discrete Equilibrium Optimizer Algorithm (NDEOA) for the classification rule generation from training CAD dataset. The proposed NDEOA is a discrete version of EOA, which use a discrete encoding of a particle for representing a classification rule; new discrete operators are also defined for the particle’s position update equation to adapt real operators to discrete space. To evaluate the proposed approach, the real world Z-Alizadeh Sani dataset has been employed. The proposed approach generate a diagnosis model composed of 17 rules, among them, five rules for the class “Normal” and 12 rules for the class “CAD”. In comparison to nine black-box and eight white-box state-of-the-art approaches, the results show that the generated diagnosis model by the proposed approach is more accurate and more interpretable than all white-box models and are competitive to the black-box models. It achieved an overall accuracy, sensitivity and specificity of 93.54%, 80% and 100% respectively; which show that, the proposed approach can be successfully utilized to generate efficient rule-based CAD diagnosis models.

Classification Comparison of Machine Learning Algorithms Using Two Independent CAD Datasets

In the last few decades, statistical methods and machine learning (ML) algorithms have become efficient in medical decision-making. Coronary artery disease (CAD) is a common type of cardiovascular disease that causes many deaths each year. In this study, two CAD datasets from different countries (TRNC and Iran) are tested to understand the classification efficiency of different supervised machine learning algorithms. The Z-Alizadeh Sani dataset contained 303 individuals (216 patient, 87 control), while the Near East University (NEU) Hospital dataset contained 475 individuals (305 patients, 170 control). This study was conducted in three stages: (1) Each dataset, as well as their merged version, was subject to review separately with a random sampling method to obtain train-test subsets. (2) The NEU Hospital dataset was assigned as the training data, while the Z-Alizadeh Sani dataset was the test data. (3) The Z-Alizadeh Sani dataset was assigned as the training data, while the NEU hospital dataset was the test data. Among all ML algorithms, the Random Forest showed successful results for its classification performance at each stage. The least successful ML method was kNN which underperformed at all pitches. Other methods, including logistic regression, have varying classification performances at every step.

Coronary Artery Disease Classification Using Deep Neural Network and Ensemble Models Optimized by Particle Swarm Optimization

Nowadays, many people are suffering from several health related issues in which Coronary Artery Disease (CAD) is an important one. Identification, prevention and diagnosis of diseases is a very challenging task in the field of medical science. This paper proposes a new feature optimization technique known as PSO-Ensemble1 to reduce the number of features from CAD datasets. The proposed model is based on Particle Swarm Optimization (PSO) with Ensemble1 classifier as the objective function and is compared with other optimization techniques like PSO-CFSE and PSO-J48 with two benchmark CAD datasets. The main objective of this research work is to classify CAD with the proposed PSO-Ensemble1 model using the Ensemble Technique.

Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network

Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.

Machine Learning Predictive Models for Coronary Artery Disease.

Coronary artery disease (CAD) is the commonest type of heart disease and over 80% of the deaths resulted from the diseases occurred in developing countries including Nigeria, with majority being in those victims are below 70 years of age. Though, CAD is not a well known disease in Nigeria but however in year 2014, 2.82% of the total of deaths occurred in the country were due to the disease. In this study, a machine leaning predictive models for CAD has been developed with diagnostic CAD dataset obtained in the two General Hospitals in Kano State—Nigeria. The dataset applied on machine learning algorithms which include support vector machine, K nearest neighbor, random tree, Naïve Bayes, gradient boosting and logistic regression algorithms to build the predictive models and the models were evaluated based accuracy, specificity, sensitivity and receiver operating curve (ROC) performance evaluation techniques. In terms of accuracy random forest-based machine learning model emerged to be the best model with 92.04%, for specificity Naive Bayes based machine learning model emerged to be the best model with 92.40%, while for sensitivity support vector machine based machine learning model emerged to be the best model with 87.34% and for ROC, random forest-based machine learning model emerged to be the best model with 92.20%. The decision tree generated with random forest machine learning algorithm which happened to be best model in terms accuracy and ROC can be converted into production rules and be used develop expert system for diagnosis of CAD patients in Nigeria.

Advanced fuzzy cognitive maps: state-space and rule-based methodology for coronary artery disease detection

According to the World Health Organization, 50% of deaths in European Union are caused by Cardiovascular Diseases (CVD), while 80% of premature heart diseases and strokes can be prevented. In this study, a Computer-Aided Diagnostic model for a precise diagnosis of Coronary Artery Disease (CAD) is proposed. The methodology is based on State Space Advanced Fuzzy Cognitive Maps (AFCMs), an evolution of the traditional Fuzzy Cognitive Maps. Also, a rule-based mechanism is incorporated, to further increase the knowledge of the proposed system and the interpretability of the decision mechanism. The proposed method is evaluated utilizing a CAD dataset from the Department of Nuclear Medicine of the University Hospital of Patras, in Greece. Several experiments are conducted to define the optimal parameters of the proposed AFCM. Furthermore, the proposed AFCM is compared with the traditional FCM approach and the literature. The experiments highlight the effectiveness of the AFCM approach, obtaining 85.47% accuracy in CAD diagnosis, showing an improvement of +7% over the traditional approach. It is demonstrated that the AFCM approach in developing Fuzzy Cognitive Maps outperforms the conventional approach, while it constitutes a reliable method for the diagnosis of Coronary Artery Disease.

Coronary artery disease diagnosis using extra tree-support vector machine: ET-SVMRBF

Coronary Artery Disease (CAD) is a type of cardiovascular disease that can lead to cardiac arrest if not diagnosed timely. Angiography is a standard method adopted to diagnose CAD. This method is an invasive method having certain side effects. So there is a need for non-invasive methods to diagnose CAD using clinical data. In this paper, authors have proposed a methodology ET-SVMRBF (Extra Tree SVM-RBF) to diagnose CAD using clinical data. The Z-Alizadeh Sani CAD data set available on University of California (UCI, Irvine), has been used for validating this methodology. The class imbalance problem in this data set has been resolved using Synthetic Minority OverSampling Technique (SMOTE). Relevant features are selected using the Extra Tree feature selection method. Authors have evaluated the performance of different classifiers Extreme Gradient Boosting (XGBoost), K-NN (K-Nearest Neighbour), Support Vector Machine-Linear (SVM-Linear) and Support Vector Machine-Radial Basis Function (SVM-RBF) on the data set. GridSearch optimisation method is used for hyperparameter optimisation. Accuracy of 95.16% is achieved by ET-SVMRBF which is higher than recent existing work in literature.

INVESTIGATING THE SYNTHETIC MINORITY CLASS OVERSAMPLING TECHNIQUE (SMOTE) ON AN IMBALANCED CARDIOVASCULAR DISEASE (CVD) DATASET

In this work, we employ the Synthetic Minority Oversampling Technique (SMOTE) to generate instances of the minority class of an imbalanced Coronary Artery Disease dataset. We firstly analyze the public dataset Z -- Alizadeh Sani, a dataset used for non-invasive prediction of CAD. We perform feature selection to exclude attributes unrelated to Coronary Artery Disease risk. The generation of new samples is performed using SMOTE, a technique commonly employed in machine learning tasks. We design Artificial Neural Networks, Decision Trees, and Support Vector Machines to classify both the original dataset and the augmented. The results demonstrate that data augmentation may be beneficial in specific cases, but it is not a panacea, and its application in a specific dataset should be carefully examined.

Genes associated with inflammation may serve as biomarkers for the diagnosis of coronary artery disease and ischaemic stroke

BackgroundThe current research aimed to expound the genes and pathways that are involved in coronary artery disease (CAD) and ischaemic stroke (IS) and the related mechanisms.MethodsTwo array CAD datasets of (GSE66360 and GSE97320) and an array IS dataset (GSE22255) were downloaded. Differentially expressed genes (DEGs) were identified using the limma package. The online tool Database for Annotation, Visualization and Integrated Discovery (DAVID) (version 6.8; david.abcc.ncifcrf.gov) was used to annotate the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses of the DEGs. A protein-protein interaction (PPI) network was constructed by Cytoscape software, and then Molecular Complex Detection (MCODE) analysis was used to screen for hub genes. The hub genes were also confirmed by RT-qPCR and unconditional logistic regression analysis in our CAD and IS patients.ResultsA total of 20 common DEGs (all upregulated) were identified between the CAD/IS and control groups. Eleven molecular functions, 3 cellular components, and 49 biological processes were confirmed by GO enrichment analysis, and the 20 common upregulated DEGs were enriched in 21 KEGG pathways. A PPI network including 24 nodes and 68 edges was constructed with the STRING online tool. After MCODE analysis, the top 5 high degree genes, including Jun proto-oncogene (JUN, degree = 9), C-X-C motif chemokine ligand 8 (CXCL8, degree = 9), tumour necrosis factor (TNF, degree = 9), suppressor of cytokine signalling 3 (SOCS3, degree = 8) and TNF alpha induced protein 3 (TNFAIP3, degree = 8) were noted. RT-qPCR results demonstrated that the expression levels of CXCL8 were increased in IS patients than in normal participants and the expression levels of SOCS3, TNF and TNFAIP were higher in CAD/IS patients than in normal participants. Meanwhile, unconditional logistic regression analysis revealed that the incidence of CAD or IS was positively correlated with the CXCL8, SOCS3, TNF and TNFAIP3.ConclusionsThe CXCL8, TNF, SOCS3 and TNFAIP3 associated with inflammation may serve as biomarkers for the diagnosis of CAD or IS. The possible mechanisms may involve the Toll-like receptor, TNF, NF-kappa B, cytokine-cytokine receptor interactions and the NOD-like receptor signalling pathways.

Decision tree-based diagnosis of coronary artery disease: CART model

Background and ObjectiveAs the most common cardiovascular defect, coronary artery disease (CAD), also called ischemic heart disease, is one of the substantial causes of death globally. Several diagnosis approaches such as baseline electrocardiography, echocardiography, magnetic resonance imaging, and coronary angiography are suggested for screening the suspected patients that may suffer from CAD. However, applying such methods may have health side effects and/or expensive costs. MethodsAs an alternative to the available diagnosis tools/methods, this research involves a decision tree learning algorithm called classification and regression tree (CART) for a simple and reliable diagnosis of CAD. Several CART models are developed based on the recently CAD dataset published in the literature. ResultsUtilizing all the features of the dataset (55 independent parameters), it was found that only 40 independent parameters influence the CAD diagnosis and consequently development of the predictive model. Based on the feature importance obtained from the first CART model, three new CART models are then developed using 18, 10, and 5 selected features. Except for the five-feature CART model, the outcomes of developed CART models demonstrate the maximum achievable accuracy, sensitivity, and specificity for CAD diagnosis (100%), while comparing the predictions with the reported targets. The error analysis reveals that the literature models including sequential minimal optimization (SMO), bagging SMO, Naïve Bayes (NB), artificial neural network (ANN), C4.5, J48, Bagging, and ANN in conjunction with the genetic algorithm (GA) do not outperform the CART methodology in classifying patients as normal or CAD. ConclusionsHence, the robustness of the tree-based algorithm in accurate and fast predictions is confirmed, implying the proposed classification technique can be successfully utilized to develop a coherent decision-making system for the CAD diagnosis.

Association between work-related features and coronary artery disease: A heterogeneous hybrid feature selection integrated with balancing approach

Coronary artery disease (CAD) is a leading cause of death worldwide and is associated with high healthcare expenditure. Researchers are motivated to apply machine learning (ML) for quick and accurate detection of CAD. The performance of the automated systems depends on the quality of features used. Clinical CAD datasets contain different features with varying degrees of association with CAD. To extract such features, we developed a novel hybrid feature selection algorithm called heterogeneous hybrid feature selection (2HFS). In this work, we used Nasarian CAD dataset, in which work place and environmental features are also considered, in addition to other clinical features. Synthetic minority over-sampling technique (SMOTE) and Adaptive synthetic (ADASYN) are used to handle the imbalance in the dataset. Decision tree (DT), Gaussian Naive Bayes (GNB), Random Forest (RF), and XGBoost classifiers are used. 2HFS-selected features are then input into these classifier algorithms. Our results show that, the proposed feature selection method has yielded the classification accuracy of 81.23% with SMOTE and XGBoost classifier. We have also tested our approach with other well-known CAD datasets: Hungarian dataset, Long-beach-va dataset, and Z-Alizadeh Sani dataset. We have obtained 83.94%, 81.58% and 92.58% for Hungarian dataset, Long-beach-va dataset, and Z-Alizadeh Sani dataset, respectively. Hence, our experimental results confirm the effectiveness of our proposed feature selection algorithm as compared to the existing state-of-the-art techniques which yielded outstanding results for the development of automated CAD systems.