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

Journal: Scientific reports
PMID: 39482391

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.

Authors

  • Muhammad Amir Khan
    Dow College of Biotechnology, Dow University of Health Sciences, Karachi, Pakistan / Department of Pharmacology, Dow College of Pharmacy, Dow University of Health Sciences, Karachi, Pakistan.
  • Tehseen Mazhar
    Department of Computer Science, Virtual University of Pakistan, Lahore, Punjab, Pakistan.
  • Muhammad Mateen Yaqoob
    Department of AI and Data Science, FAST-National university of Computer and emerging sciences, Islamabad, Pakistan.
  • Muhammad Badruddin Khan
    Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
  • Abdul Khader Jilani Saudagar
    Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11432, Riyadh, Saudi Arabia.
  • Yazeed Yasin Ghadi
    Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi, UAE.
  • Umar Farooq Khattak
    School of Information Technology, UNITAR International University, Kelana Jaya, 47301, Petaling Jaya, Malaysia.
  • Mohammad Shahid
    Department of Agricultural Microbiology, Faculty of Agricultural Sciences, Aligarh Muslim University, Aligarh 202002, Uttar Pradesh, India. Electronic address: shahidfaiz5@gmail.com.

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