Optimizing unsupervised feature engineering and classification pipelines for differentiated thyroid cancer recurrence prediction.

Journal: BMC medical informatics and decision making
PMID: 40361143

Abstract

BACKGROUND: Differentiated thyroid cancer (DTC) is a common endocrine malignancy with rising incidence and frequent recurrence, despite a generally favorable prognosis. Accurate recurrence prediction is critical for guiding post-treatment strategies. This study aimed to enhance predictive performance by refining feature engineering and evaluating a diverse ensemble of machine learning models using the UCI DTC dataset.

Authors

  • Emmanuel Onah
    Department of Pharmaceutical and Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Nigeria, Nsukka, Enugu State, 410001, Nigeria. emmanuel.onah.187260@unn.edu.ng.
  • Uche Jude Eze
    College of Pharmacy, Ohio State University, Ohio, 43210, USA. eze.18@buckeyemail.osu.edu.
  • Abdullahi Salahudeen Abdulraheem
    Department of Pharmacognosy, Faculty of Pharmacy, University of Lagos, Akoka, Yaba, Lagos, 101017, Nigeria.
  • Ugochukwu Gabriel Ezigbo
    School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
  • Kosisochi Chinwendu Amorha
    Department of Clinical Pharmacy and Pharmacy Management, Faculty of Pharmaceutical Sciences, University of Nigeria, Nsukka, Enugu State, 410001, Nigeria.
  • Fidele Ntie-Kang
    Department of Pharmaceutical Chemistry, Martin-Luther University of Halle-Wittenberg, Wolfgang-Langenbeck-Str. 4, 06120, Halle, Saale, Germany; Department of Chemistry, Faculty of Science, University of Buea, P. O. Box 63, Buea, Cameroon. Electronic address: ntiekfidele@gmail.com.

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