A machine learning based variable selection algorithm for binary classification of perinatal mortality.
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 regressio...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315498 |
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| _version_ | 1832540279797710848 |
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| author | Maryam Sadiq Ramla Shah |
| author_facet | Maryam Sadiq Ramla Shah |
| author_sort | Maryam Sadiq |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c7c614258faf4a70a9f7097d3f900486 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-c7c614258faf4a70a9f7097d3f9004862025-02-05T05:31:17ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031549810.1371/journal.pone.0315498A machine learning based variable selection algorithm for binary classification of perinatal mortality.Maryam SadiqRamla ShahThe 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.https://doi.org/10.1371/journal.pone.0315498 |
| spellingShingle | Maryam Sadiq Ramla Shah A machine learning based variable selection algorithm for binary classification of perinatal mortality. PLoS ONE |
| title | A machine learning based variable selection algorithm for binary classification of perinatal mortality. |
| title_full | A machine learning based variable selection algorithm for binary classification of perinatal mortality. |
| title_fullStr | A machine learning based variable selection algorithm for binary classification of perinatal mortality. |
| title_full_unstemmed | A machine learning based variable selection algorithm for binary classification of perinatal mortality. |
| title_short | A machine learning based variable selection algorithm for binary classification of perinatal mortality. |
| title_sort | machine learning based variable selection algorithm for binary classification of perinatal mortality |
| url | https://doi.org/10.1371/journal.pone.0315498 |
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