Scientific Reports (Oct 2024)

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

  • Muhammad Amir Khan,
  • Tehseen Mazhar,
  • Muhammad Mateen Yaqoob,
  • Muhammad Badruddin Khan,
  • Abdul Khader Jilani Saudagar,
  • Yazeed Yasin Ghadi,
  • Umar Farooq Khattak,
  • Mohammad Shahid

DOI
https://doi.org/10.1038/s41598-024-78021-1
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 15

Abstract

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Abstract 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.

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