Analysis of Quality of Life among Caregivers of Patients with Thalassemia Major by Cross-Sectional Examination at a Tertiary Care Hospital

Introduction: Patients with thalassemia major are among the vulnerable subsets of population who need to be frequently admitted for transfusions. Thus, their caregivers are at an elevated risk of developing psychiatric comorbidities as they better understand the course of disease, prognosis and anticipate the impending burden. There is a lacuna in literature about our understanding of these caregivers’ experience with psychiatric illness, quality of life, coping skills and the effect these have on them as well as their patient

Efficient genomic selection using ensemble learning and ensemble feature reduction

Genomic selection (GS) is a popular breeding method that uses genome-wide markers to predict plant phenotypes. Empirical studies and simulations have shown that GS can greatly accelerate the breeding cycle, beyond what is possible with traditional quantitative trait locus (QTL) approaches. GS is a regression problem, where one often uses SNPs to predict the phenotypes. Since the SNP data are extremely high-dimensional, of the order of 100 K dimensions, it is difficult to make accurate phenotypic predictions. Moreover, finding the optimal prediction model is computationally very costly. Out of thousands of SNPs, usually only a few influence a particular phenotypic trait. We first of all show how ensemble-based regression techniques give better prediction accuracy compared to traditional regression methods, which have been used in existing papers. We then further improve the prediction accuracy by using an ensemble of feature selection and feature extraction techniques, which also reduces the time to compute the regression model parameters. We predict three traits: grain yield, time to 50% flowering and plant height for which the existing methods give an accuracy of 0.304, 0.627 and 0.341, respectively. Our proposed regression model gives an accuracy of 0.330, 0.674 and 0.458 for these traits. Additionally, we also propose a computationally efficient regression model that reduces the computation time by as much as 90% and gives an accuracy of 0.342, 0.580 and 0.411, respectively