Enhancing Coronary Artery Disease Detection with a Hybrid Machine Learning Approach: Integrating K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) Algorithms


Authors : Abi Izang Igyem; Fatima Umar Zambuk; Badamasi Imam Yau; Mustapha Abdulrahman Lawal; Sandra Hoommi Hoomkwap; Fatima Shittu; Atiku Baba Shidawa; Ismail Zahraddeen Yakubu

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://tinyurl.com/226utjfu

Scribd : https://tinyurl.com/y6wyyb9c

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR2097

Abstract : Recent studies have identified coronary artery disease (CAD) as a leading cause of death globally. Early detection of CAD is crucial for reducing mortality rates. However, accurately predicting CAD poses challenges, particularly in treating patients effectively before a heart attack occurs due to the complexity of data and relationships in traditional methodologies. This research has successfully developed a machine learning model for CAD prediction by combining K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) Classifier techniques. The model, trained and tested on a dataset of 918 samples (508 with cardiac issues and 410 healthy cases), achieved an accuracy of 82% for KNN, 84.3% for SVM, and 88.7% for the hybrid model after rigorous training and testing.

Keywords : Coronary Artery Disease, Machine Learning and Heart Disease.

Recent studies have identified coronary artery disease (CAD) as a leading cause of death globally. Early detection of CAD is crucial for reducing mortality rates. However, accurately predicting CAD poses challenges, particularly in treating patients effectively before a heart attack occurs due to the complexity of data and relationships in traditional methodologies. This research has successfully developed a machine learning model for CAD prediction by combining K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) Classifier techniques. The model, trained and tested on a dataset of 918 samples (508 with cardiac issues and 410 healthy cases), achieved an accuracy of 82% for KNN, 84.3% for SVM, and 88.7% for the hybrid model after rigorous training and testing.

Keywords : Coronary Artery Disease, Machine Learning and Heart Disease.

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