Abstract
The frequency and timing of antenatal care visits are observed to be the significant factors of infant and maternal morbidity and mortality. The present research is conducted to determine the risk factors of reduced antenatal care visits using an optimized partial least square regression model. A data set collected during 2017-2018 by Pakistan Demographic and Health Surveys is used for modeling purposes. The partial least square regression model coupled with rank correlation measures are introduced for improved performance to address ranked response. The proposed models included PLSρs, PLSτA, PLSτB, PLSτC, PLS D, PLSτGK, PLS G, and PLS U. Three filter-based factor selection methods are executed, and leave-one-out cross-validation by linear discriminant analysis is measured on predicted scores of all models. Finally, the Monte Carlo simulation method with 10 iterations of repeated sampling for optimization of validation performance is applied to select the optimum model. The standard and proposed models are executed over simulated and real data sets for efficiency comparison. The PLSρs is found to be the most appropriate proposed method to model the observed ranked data set of antenatal care visits based on validation performance. The optimal model selected 29 influential factors of inadequate use of antenatal care. The important factors of reduced antenatal care visits included women's educational status, wealth index, total children ever born, husband's education level, domestic violence, and history of cesarean section. The findings recommended that partial least square regression algorithms coupled with rank correlation coefficients provide more efficient estimates of ranked data in the presence of multicollinearity.
Highlights
Pakistan sets targets to minimize the maternal mortality ratio (MMR) to 140 per 100,000 live births by 2015 by increasing skilled birth attendants and improving access to reproductive health care as the fifth Millennium Development Goal (MDG) for improving maternal health suggested
Spearman’s coefficient, Kendall’s coefficient-A, Kendall’s coefficient-B, Stuart-Kendall’s, Somers’ delta, GoodmanKruskal’s tau, Goodman-Kruskal’s gamma, and Thiel’s U coefficient are used as loading weights of the partial least square (PLS) algorithm to fit oversimulated data set to observe the variation in performance of standard and proposed models based on Akaike information criterion (AIC)
The Partial Least Square Regression (PLSR) model with τGK as modified loading weight showed optimum performance compared to eight other models without integrating any variable selection method
Summary
Pakistan sets targets to minimize the maternal mortality ratio (MMR) to 140 per 100,000 live births by 2015 by increasing skilled birth attendants and improving access to reproductive health care as the fifth Millennium Development Goal (MDG) for improving maternal health suggested. Pakistan has endorsed the Sustainable Development Goals (SDGs), committing to decrease the MMR to 70 per 100,000 live births by 2030 by increasing skilled birth attendance, facilitation to modern contraception, and extending coverage of health workers. The Government of Pakistan took initiatives and made good progress in maternal health indicators during the last decade, and a significant decline was reported in MMR from 276 to 178 [1, 2]. Pregnancy-related morbidity and mortality can be reduced by improving access and facilitation to maternal health care services. Several studies have assessed the significant influential factors of antenatal care
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