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A machine learning based variable selection algorithm for binary classification of perinatal mortality

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  • Maryam Sadiq
  • Ramla Shah

Abstract

The identification of significant predictors with higher model performance is the key objective in classification domain. A machine learning-based variable selection technique termed as CARS-Logistic model is proposed by coupling competitive adaptive re-weighted sampling(CARS) and logistic regression for binary classification. Based on five assessment criteria, the proposed method is found to be more efficient than Forward selection logistic regression model. The CARS-Logistic model is executed to determine the significant factors of perinatal mortality in Pakistan. The identified hazards communicated social, cultural, financial, and health-related characteristics which contain key information about perinatal mortality in Pakistan for policymakers.

Suggested Citation

  • Maryam Sadiq & Ramla Shah, 2025. "A machine learning based variable selection algorithm for binary classification of perinatal mortality," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-12, January.
    • Handle: RePEc:plo:pone00:0315498
    DOI: 10.1371/journal.pone.0315498
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