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.

Factor discovery of high-dimensional data is a crucial problem and extremely challenging from a scientific viewpoint with enormous applications in research studies. In this study, the main focus is to introduce the improved subset factor selection method and hence, 9 subset selection methods for partial least squares regression (PLSR) based on filter factor subset selection approach are proposed. Existing and proposed methods are compared in terms of accuracy, sensitivity, F1 score and number of selected factors over the simulated data set. Further, these methods are practiced on a real data set of nutritional status of children obtained from Pakistan Demographic and Health Survey (PDHS) by addressing performance using a Monte Carlo algorithm. The optimal method is implemented to assess the important factors of nutritional status of children. Dispersion importance (DIMP) factor selection index for PLSR is observed to be a more efficient method regarding accuracy and number of selected factors. The recommended factors contain key information for the nutritional status of children and could be useful in related research.

Survival systems are difficult to analyze in the presence of extreme observations and multicollinearity. Finding appropriate models that provide a robust description of such survival systems and that address the smooth hazards in the context of covariates can be challenging given the sheer number of possibilities. Survival time algorithms that evaluate the efficiency of models in the presence of extreme observations over different datasets provide an effective tool to identify robust systems. However, the existing algorithms addressing the analysis of survival systems are limited in long-term evaluations. Therefore, an algorithm that can analyze survival time response on high-dimensional complex survival systems having extreme observations is developed which explores large margins dynamically. This algorithm is developed as a conjugate of flexible parametric models and partial least squares to estimate smooth, flexible, and robust functions to extrapolate the survival model in long-term evaluations in the presence of extreme observations. The algorithm is tested and validated using four distributions based on a simulated dataset generated from the Weibull distribution and compared with partial least squares-Cox regression. The comparison shows its flexibility and efficiency in handling different survival systems in the presence of extreme values. The algorithm is also used to analyze four real datasets of breast cancer survival time, each containing seven gene signatures. The coefficients of significant genes for each dataset are estimated. The flexibility in handling various distributions as parametric survival models supports the application of the algorithm to a large variety of different survival problems and represents a robust statistical framework for survival analysis in the presence of extreme observations.

BackgroundHigh-quality prenatal care has a significant positive impact on maternal and infant health as it helps timely diagnosis and treatment of pregnancy complications.ObjectiveTo examine factors associated with the utilization of maternal health care using the optimal count regression model.MethodsA sample of 16,314 women of reproductive ages (15–49) was used. Andersen and Newman's behavioral model of health services utilization was employed for the selection of covariates. Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial (ZINB), Poisson hurdle, and negative binomial hurdle models were fitted and compared to identify the best model. Maternal health care utilization is found associated with maternal age and education, area of residence, domestic violence, the income level of family, access to media, knowledge about AIDS, parity, birth order, and having a child who later died.ResultsZINB model is found to be best fitted for the observed data resulting strong influence of mother's education and income level of the family on maternal health care utilization.ConclusionInterventions to improve maternal care services utilization should address individuals and systems to reduce social and economic marginalization.