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

Sayadi, Mohammadjavad; Varadarajan, Vijayakumar; Sadoughi, Farahnaz; Chopannejad, Sara; Langarizadeh, Mostafa

doi:10.3390/life12111933

Cited by 14 publications

(8 citation statements)

References 36 publications

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“…663 by all authors. As additional selection criteria, [11], [15], [21], [28], [31], [37], [38] used F1 score, sensitivity, and specificity.…”

Section: Review Of the Articles On Cad Prediction Using Machine Learn...mentioning

confidence: 99%

Potential directions on coronary artery disease prediction using machine learning algorithms: A survey

Vijayaraj,

Pasupathi

2024

IJ-AI

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<p>Coronary artery disease (CAD) is the most ubiquitous and protuberant cause of fatal death. The hit in mortality rate is because of certain lifestyle variables including unhealthy diet, usage of tobaccos and drugs, physical inactivity, and environmental pollution. Traditional screening tests including computed tomography, angiography, electrocardiography, and magnetic resonance imaging are employed for diagnosis and would necessitate more manpower. Machine learning (ML) has been utilized in healthcare to create early predictions from massive volumes of data. The Scopus, Web of Science databases were exhaustively searched utilizing a search strategy that comprised CAD prediction, cardiac illness detection, and heart disease categorization. After applying the inclusion and exclusion criteria to the 99 articles obtained, the population of the study was composed of 30 articles. This review study offers an organized look at the articles published in ML-based CAD detection and classification models that include clinical variables. The use of ML could produce amazing results in CAD detection, as evidenced by the classifiers random forest, decision tree, and k-nearest-neighbour with accuracy being >90%. The use of ML in CAD diagnosis lowers false-positive, and false-negative errors, and presents a special opportunity by providing patients quick, and affordable diagnostic services.</p>

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“…663 by all authors. As additional selection criteria, [11], [15], [21], [28], [31], [37], [38] used F1 score, sensitivity, and specificity.…”

Section: Review Of the Articles On Cad Prediction Using Machine Learn...mentioning

confidence: 99%

Potential directions on coronary artery disease prediction using machine learning algorithms: A survey

Vijayaraj,

Pasupathi

2024

IJ-AI

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“…Combine feature subset selection with optimization techniques to increase forecast accuracy. This technology is further employed in conjunction with classification and clustering algorithms to increase the accuracy of classification approaches [25]. Utilizing the XGBoost Classifier, the specificity and sensitivity were 88.79% and 87.17%, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Ensemble Stacking Techniques for Coronary Artery Disease Prediction Using Machine Learning Algorithms

CMM. Mansoor

2023

IJRITCC

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Throughout history, humanity has been plagued by several outbreaks that have claimed numerous lives. Since coronary artery disease is among the most fatal illnesses that humanity has faced in the modern era, it has been recognized in our time. It links several Coronary Artery Disease (CAD) risk factors to the critical requirement for precise, reliable, and workable methods for early identification and management. In light of this, we suggest a technique called Hybrid Ensemble Stacking that combines Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Ada Boosting for the prediction of CAD illnesses. To combine the forecasts of the basis models, a meta-logistic regression model is utilized. According to a quantitative study, the ensemble model and brute force feature selection method together produce a classification accuracy for heart disease of up to 92.66%. The suggested stacking model has demonstrated its effectiveness and outperforms current methods in the categorization of cardiac disorders. Several classification issues have been solved successfully using ensemble techniques. The suggested method was constructed using the Sani dataset, which contains 303 nearly completed records. Using Min-Max Normalization, the data are pre-processed to making it suitable for a Machine Learning (ML) model. SMOTE and SelectKBest technique were applied to increases the accuracy and efficiency of a model. Using the metrics such as accuracy, precision, recall, F1, ROC and log-loss, the outcomes produced by the suggested model had the greatest performance.

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“…If diagnosed in early stages, the possibility of avoiding cardiac disease is high for both patients and specialists. Medicine provides information about early risk factors for heart attacks and taking precautions against them before a heart attack occurs [3,4].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Heart Disease Using Data Mining Techniques: A Systematic Review of Influential Factors and Outcomes

Gozali,

Hajesmaeel-Gohari,

Khademvatani

et al. 2024

Front Health Inform

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Introduction: Heart disease is a major public health concern with millions of reported deaths annually. Data mining techniques have received attention in recent years as a tool aiding diagnosis and prediction of heart disease cases. This systematic review examines the application of data mining methods to cardiac disease diagnosis in order to identify specific types of heart-related disease that are diagnosed using data mining techniques as well as the most successful data mining methods.Material and Methods: This study involved a systematic review of IEEE, Science Direct, Google Scholar, Web of Science, Scopus and MEDLINE databases from 2008 until April 2023. Inclusion criteria were original papers that used data mining methods for heart disease diagnosis. Non-English papers, those without full text, studies conducted on animals, and other types of papers (conference abstracts and letters) were excluded from the study. All the retrieved references were then assessed by title and abstract according to PRISMA, after which full texts of relevant articles were analyzed. The final sample comprised of 47 articles.Results: Various classification methods have been utilized to diagnose heart-related disease using different mining tools, with genetic neural network data mining method having the highest accuracy among the studied techniques. Results show that predicting cardiac disease is the most commonly performed task. The demographic, bio-clinical, personal and exercise-related attributes, as well as other features used for classification were identified. The findings suggest that data mining methods hold great potential for detecting and preventing heart disease on both individual and population scales.Conclusion: The study findings have implications for the prevention and treatment of cardiac disease, especially in high-risk individuals. Data mining methods can be widely applied to detect and prevent heart disease on a population scale, as well as supporting decisions for the most suitable treatment for individual patients to prevent death and reduce treatment costs.

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

Cited by 14 publications

References 36 publications

Potential directions on coronary artery disease prediction using machine learning algorithms: A survey

Potential directions on coronary artery disease prediction using machine learning algorithms: A survey

Hybrid Ensemble Stacking Techniques for Coronary Artery Disease Prediction Using Machine Learning Algorithms

Diagnosis of Heart Disease Using Data Mining Techniques: A Systematic Review of Influential Factors and Outcomes

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