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With the development of high-throughput technologies for producing genomic data, more advanced statistical methods such as regularization and machine learning techniques have been adapted to survival analysis. However, the clinical information such as age, gender and medical history plays a critical role in constructing a survival prediction model. Machine learning technique such as the support vector machine (SVM) can improve the predictability of the survival model by using the available clinical information. When implementing SVM for a predictive survival model, the clinical kernel was proposed by Daemen et al. by equalizing the influence of clinical variables and taking account of the range of these variables. However, this clinical kernel uses the same weight for all clinical variables without considering the different effect of those variables on the survival time. In this study, we proposed a simple kernel, called ensemble kernel, by combining a clinical kernel with model fitting. Since the proposed ensemble kernel is based on model fitting, two different kernels are considered by using either Cox model or accelerated failure time (AFT) model. We compare the performance of these two ensemble kernels with that of the linear kernel and the clinical kernel by the concordance index (C-index) using the four real data sets. While both linear and clinical kernels use all clinical variables in defining global kernels, the proposed two ensemble kernels can use only significant variables from either a Cox model or an AFT model. The comparative result shows that the proposed two ensemble kernels perform similarly as the existing clinical kernel does and the performance of four kernels vary according to data sets. [ABSTRACT FROM AUTHOR]

Introduction: Duration of breastfeeding is the length of the time that infants who were initially breastfed continue to receive breast milk until weaning. The duration of breastfeeding is important for a child's health, growth, and development. However, the duration of breastfeeding decreases from time to time and further leads children to be exposed to malnutrition (stunting, wasting, and weight loss). Children who did not get enough breastfeeding are also exposed to different diseases. Previous studies used a simple survival model and didn't see the shared frailty model on the variable of interest. Therefore, the current study aimed to investigate the factors affecting the duration of breastfeeding among Ethiopian women of reproductive age with babies. Methods: A cross-sectional study design was conducted on 15,400 women of childbearing age with babies in nine regional states and two city administrations. The data source for the analysis was the 2016 EDHS data. The Cox-proportional hazard model, AFT, and parametric shared frailty models were conducted for the current investigation. Weibull-gamma shared frailty model was in favor of others for current data analysis. Results: Among the covariates, women living in urban area (Φ = 0.96; 95% CI; (0.94,0.97); p-value = 0.001), non-educated women(Φ = 1.03; 95% CI; (1.00,1.06); p-value = 0.039), primary educated women (Φ = 1.13; 95% CI; (1.11,1.15); p-value < 0.001), age of a child (Φ = 0.99; 95% CI; (0.76.0.99); p-value < 0.001) and non-smoker mothers (Φ = 1.60; 95% CI; (1.57, 1.63); p-value < 0.001),birth interval between 2–3 years(Φ = 1.02; 95% CI;(1.09, 1.25, p-value = 0.027), birth interval, > 3 years(Φ = 1.28; 95% CI; (1.06, 1.43); p-value < 0.01 significantly affected the duration of breastfeeding. The median survival time of breastfeeding of women of reproductive age with babies considered under study was 23.4 months. Clustering had a significant effect on the variable of interest. Conclusion: Residence area, level of education, age of the child, smoking status of women, and birth interval of successive birth significantly affected the duration of breastfeeding in the current investigation. Hence, the health staff should conduct health-related education for young women, educated women, urban women, smoker women, and women with a shorter interval of birth to increase the women's attitude and awareness towards the use of long-duration of breastfeeding. [ABSTRACT FROM AUTHOR]

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