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Knowledge Mining from Health Data: Application of Feature Selection Approaches

In: Novel Financial Applications of Machine Learning and Deep Learning

Author

Listed:
  • Md. Rabiul Auwul

    (American International University-Bangladesh)

  • Md. Ajijul Hakim

    (Travelex Qatar, Golbex Business Center)

  • Fahmida Tasnim Dhonno

    (Hajee Mohammad Danesh Science and Technology University)

  • Nusrat Afrin Shilpa

    (Hajee Mohammad Danesh Science and Technology University)

  • Mohammad Zoynul Abedin

    (Teesside University International Business School, Teesside University)

Abstract

This paper aims to measure the performance of feature selection approaches for mining knowledge from health datasets. We compare seven popular knowledge mining approaches, namely relaxed Lasso, random forest, ReliefF, OneR, information gain, T-test, and Chi-squared test. The support vector machine (SVM) classifier applies to determine the accuracy and area under the curve (AUC) values of the knowledge miners. We use six popular Affymetrix and cDNA datasets. The results reveal that the relaxed lasso works well with Affymetrix, and the relaxed Lasso with random forest approaches perform well with the cDNA datasets. This paper will enrich the existing literature and assist to identify the best feature for knowledge mining in the health informatics domain.

Suggested Citation

  • Md. Rabiul Auwul & Md. Ajijul Hakim & Fahmida Tasnim Dhonno & Nusrat Afrin Shilpa & Mohammad Zoynul Abedin, 2023. "Knowledge Mining from Health Data: Application of Feature Selection Approaches," International Series in Operations Research & Management Science, in: Mohammad Zoynul Abedin & Petr Hajek (ed.), Novel Financial Applications of Machine Learning and Deep Learning, pages 217-231, Springer.
    • Handle: RePEc:spr:isochp:978-3-031-18552-6_13
    DOI: 10.1007/978-3-031-18552-6_13
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