For support vector machine (SVM) learning, least squares SVM (LSSVM), derived by duality LSSVM (D-LSSVM), is a widely used model, because it has an explicit solution. One obvious limitation of the model is that the solution lacks sparseness, which limits it from training large-scale problems efficiently. In this paper, we derive an equivalent LSSVM model in primal space LSSVM (P-LSSVM) by the representer theorem and prove that P-LSSVM can be solved exactly at some sparse solutions for problems with low-rank kernel matrices. Two algorithms are proposed for finding the sparse (approximate) solution of P-LSSVM by Cholesky factorization. One is based on the decomposition of the kernel matrix K as P P(T) with the best low-rank matrix P approximately by pivoting Cholesky factorization. The other is based on solving P-LSSVM by approximating the Cholesky factorization of the Hessian matrix with rank-one update scheme. For linear learning problems, theoretical analysis and experimental results support that P-LSSVM can give the sparsest solutions in all SVM learners. Experimental results on some large-scale nonlinear training problems show that our algorithms, based on P-LSSVM, can converge to acceptable test accuracies at very sparse solutions with a sparsity level <1%, and even as little as 0.01%. Hence, our algorithms are a better choice for large-scale training problems.

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Fulminant hepatitis is likely to occur in late pregnancy with more severe complications, which significantly influences maternity, perinatal fetus, and newborn.

Objective: To evaluate the usefulness of a fasting plasma glucose (FPG) at the first trimester in predicting gestational diabetes mellitus (GDM) and the association between FPG and adverse pregnancy outcomes. Methods: The levels of FPG in women with singleton pregnancies were measured at 9-13+6 weeks. A two hour 75-g oral glucose tolerance test (OGTT) was completed at 24-28 weeks and the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria was used. Adverse pregnancy outcomes were assessed and recorded. Results: Among 2112 pregnant women enrolled in the study, 224 (10.6%) subjects were diagnosed with GDM. The AUC for FPG in predicting GDM was 0.63 (95% CI 0.61- 0.65) and the optimal cutoff value was 4.5 mmol/L (sensitivity 64.29% and specificity 56.45%). Higher first-trimester FPG increased the prevalence of GDM, large for gestational age (LGA) and assisted vaginal delivery and/or cesarean section (all P < 0.05). Conclusion: FPG at first trimester could be used to predict GDM and higher first-trimester FPG was associated with adverse pregnancy outcomes. How to cite this:Li P, Lin S, Li L, Cui J, Zhou S, Fan J. First-trimester fasting plasma glucose as a predictor of gestational diabetes mellitus and the association with adverse pregnancy outcomes. Pak J Med Sci. 2019;35(1):---------. doi: https://doi.org/10.12669/pjms.35.1.216 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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