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

• Published in: Scientific Reports
• Main Author: Khan M.A.; Mazhar T.; Mateen Yaqoob M.; Badruddin Khan M.; Jilani Saudagar A.K.; Ghadi Y.Y.; Khattak U.F.; Shahid M.
• Format: Article
• Language: English
• Published: Nature Research 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208290452&doi=10.1038%2fs41598-024-78021-1&partnerID=40&md5=041288d45781f77cf548351d46cbc63e

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. © The Author(s) 2024.