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A Novel Feature Selection Method for Classification of Medical Data Using Filters, Wrappers, and Embedded Approaches

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  • Saba Bashir
  • Irfan Ullah Khattak
  • Aihab Khan
  • Farhan Hassan Khan
  • Abdullah Gani
  • Muhammad Shiraz

Abstract

Feature selection is the process of identifying the most relevant features from the given data having a large feature space. Microarray datasets are comprised of high‐quality features and very few samples of data. Feature selection is performed on such datasets to identify the optimal feature subset. The major goal of feature selection is to improve the accuracy by identifying a minimal feature subset. For this purpose, the proposed research focused on analyzing and identifying effective feature selection algorithms. A novel framework is proposed which utilizes different feature selection methods from filters, wrappers, and embedded algorithms. Furthermore, classification is then performed on selected features to classify the data using a support vector machine (SVM) classifier. Two publically available benchmark datasets are used, i.e., the Microarray dataset and the Cleveland Heart Disease dataset, for experimentation and analysis, and they are archived from the UCI data repository. The performance of SVM is analyzed using accuracy, sensitivity, specificity, and f‐measure. The accuracy of 94.45% and 91% is achieved on each dataset, respectively.

Suggested Citation

  • Saba Bashir & Irfan Ullah Khattak & Aihab Khan & Farhan Hassan Khan & Abdullah Gani & Muhammad Shiraz, 2022. "A Novel Feature Selection Method for Classification of Medical Data Using Filters, Wrappers, and Embedded Approaches," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:8190814
    DOI: 10.1155/2022/8190814
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    References listed on IDEAS

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    1. N. Hoque & H.A. Ahmed & D.K. Bhattacharyya & J.K. Kalita, 2016. "A Fuzzy Mutual Information-based Feature Selection Method for Classification," Fuzzy Information and Engineering, Taylor & Francis Journals, vol. 8(3), pages 355-384, September.
    2. Taqwa Ahmed Alhaj & Maheyzah Md Siraj & Anazida Zainal & Huwaida Tagelsir Elshoush & Fatin Elhaj, 2016. "Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-18, November.
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