ArticlePloS one2019
Identifying the factors associated with cesarean section modeled with categorical correlation coefficients in partial least squares.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Health system drivers of caesarean deliveries in south Asia: a scoping review.The Lancet regional health. Southeast Asia · 2025Review
- Factor selection techniques based on partial least squares for classification to determine the risk factors of sexually transmitted infections.Scientific reports · 2025Article
- Prevalence and factors associated with caesarean section among Tanzanian women of reproductive age: evidence from the 2022 Tanzania demographic and health survey data.BMC public health · 2025Article
- Article
- Article
- Feasibility of a birth-cohort in Pakistan: evidence for better lives study.Pilot and feasibility studies · 2022Article
- Complex Survival System Modeling for Risk Assessment of Infant Mortality Using a Parametric Approach.Computational and mathematical methods in medicine · 2022Article
- Modeling the Ranked Antenatal Care Visits Using Optimized Partial Least Square Regression.Computational and mathematical methods in medicine · 2022Article
- The Partial Least Squares Spline Model for Public Health Surveillance Data.Computational and mathematical methods in medicine · 2022Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cesarean section (CS) is associated with maternal morbidity and mortality in developing countries. This study is conducted to assess factors associated with CS in Pakistan using partial least squares (PLS) algorithm, where categorical factors are modeled. Nationally representative maternal data from Pakistan Demographic and Health Surveys (PDHS) conducted during 2012-2013 is used in this study. Among correlation coefficient based PLS regression proposed algorithms for categorical factors, Pearson's Contingency Coefficient (CC) PLS coupled with loading weight (LW) appeared to be the most efficient method in terms of model performance and influential factor selection. Region of residence, type of place of residence, mother's and her partner's level of education, wealth index, year of birth, previous terminated pregnancy, use of contraception, prenatal care provided by a doctor and nurse/midwife/LHV (lady health visitor), assistance provided by a nurse/midwife/LHV,number of antenatal visits, size of child, antenatal care provided by government hospital, transport facility for medical care, baby birth status, mother's age at first birth, preceding birth interval and vaccination of hepatitis B-1 and B2 are found to be significantly affecting the CS delivery method. Correlation coefficient based PLS regression algorithms may serve more efficiently as a multivariate technique to treat high-dimensional categorical data.
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