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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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A machine learning based variable selection algorithm for binary classification of perinatal mortality.

Maryam Sadiq1, Ramla Shah1

  • 1Department of Statistics, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan.

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The new CARS-Logistic model efficiently identifies key factors influencing perinatal mortality. This machine learning approach offers better performance for policymakers to reduce infant deaths.

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Area of Science:

  • Machine Learning
  • Biostatistics
  • Public Health

Background:

  • Accurate identification of predictors is crucial for classification models.
  • Existing methods like Forward Selection logistic regression have limitations.
  • Perinatal mortality remains a significant public health concern in many regions.

Purpose of the Study:

  • To propose a novel machine learning-based variable selection technique, the CARS-Logistic model.
  • To evaluate the efficiency of the CARS-Logistic model against traditional methods.
  • To identify significant predictors of perinatal mortality in Pakistan.

Main Methods:

  • Coupling Competitive Adaptive Re-weighted Sampling (CARS) with logistic regression for binary classification.
  • Utilizing five assessment criteria to evaluate model performance.
  • Applying the CARS-Logistic model to a dataset on perinatal mortality in Pakistan.

Main Results:

  • The CARS-Logistic model demonstrated superior efficiency compared to the Forward selection logistic regression model.
  • The model successfully identified significant predictors of perinatal mortality.
  • Identified risk factors encompassed social, cultural, financial, and health-related characteristics.

Conclusions:

  • The CARS-Logistic model is an effective tool for variable selection in binary classification tasks.
  • The identified factors provide crucial insights for developing targeted interventions to reduce perinatal mortality in Pakistan.
  • Findings offer valuable information for policymakers to address the social, cultural, financial, and health determinants of perinatal mortality.