Feature Selection Based Data Mining Approach for Coronary Artery Disease Diagnosis

Akyol, Kemal

doi:10.21541/apjes.899055

“…It is planned to use huge datasets with more features in the future to get better performance and more insightful results. It also focuses on deep learning technique experiments between [28].…”

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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“…It is planned to use huge datasets with more features in the future to get better performance and more insightful results. It also focuses on deep learning technique experiments between [28].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Sarat Kumar Chettri, HMM. Naleer

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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Walrus Optimization Algorithm based Support Vector Machine for Feature Selection and Classification in Coronary Artery Disease

Sharma,

Anjali,

Kumar

et al. 2024

2024 International Conference on Integrated Circuits, Communication, and Computing Systems (ICIC3S)

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Feature Selection Based Data Mining Approach for Coronary Artery Disease Diagnosis

Cited by 3 publications

References 35 publications

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

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

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

Walrus Optimization Algorithm based Support Vector Machine for Feature Selection and Classification in Coronary Artery Disease

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