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

• 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
• Subjects: Science & Technology - Other Topics
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001346350300026

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.