Optimal feature selection for heart disease prediction using modified Artificial Bee colony (M-ABC) and K-nearest neighbors (KNN)

Heart disease is a complex and widespread illness that affects a significant number of people worldwide. Machine learning provides a way forward for early heart disease diagnosis. A classification model has been developed for the present study to predict heart disease. The attribute selection was do...

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Bibliographic Details
Published in:SCIENTIFIC REPORTS
Main Authors: Khan, Muhammad Amir; Mazhar, Tehseen; Mateen Yaqoob, Muhammad; Badruddin Khan, Muhammad; Jilani Saudagar, Abdul Khader; Ghadi, Yazeed Yasin; Khattak, Umar Farooq; Shahid, Mohammad
Format: Article
Language:English
Published: NATURE PORTFOLIO 2024
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Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001346350300026
Description
Summary:Heart disease is a complex and widespread illness that affects a significant number of people worldwide. Machine learning provides a way forward for early heart disease diagnosis. A classification model has been developed for the present study to predict heart disease. The attribute selection was done using a modified bee algorithm. Using the proposed model, practitioners can accurately predict heart disease and make informed decisions about patient health. In our study, we have proposed a framework based on Modified Artificial Bee Colony (M-ABC) and k-Nearest Neighbors (KNN) for predicting the optimal feature selection to obtain better accuracy. Using a modified bee algorithm, this paper focuses on identifying the optimal subset of attributes from the dataset. Specifically, during the classification-training phase, only the features that provide significant information are retained. The proposed study not only improves classification accuracy but also reduces training time for classifiers.
ISSN:2045-2322
DOI:10.1038/s41598-024-78021-1