ArticlePloS one2025
A machine learning based variable selection algorithm for binary classification of perinatal mortality.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Comparing the use of supervised machine learning variable selection methods in the context of two-group classification in the psychological and health sciences.Frontiers in psychology · 2026Article
- Margin weighted robust discriminant score for feature selection in imbalanced gene expression classification.PloS one · 2025Article
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2 authors.
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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.
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