Search

India has witnessed a dramatic expansion of higher education, and women have emerged as noteworthy winners in the process. This paper focuses on the role of female college education on four dimensions of marriage: age at marriage, autonomy in the choice of spouse, work and financial empowerment, and quality of marital relationship. The study uses a sample of 35,561 currently married women from the 2011-2012 wave of the nationally representative India Human Development Survey (IHDS). It demonstrates that higher education, particularly college education, enables women to lead lives substantively different from their less-educated peers. College-educated women marry at later ages, enjoy greater autonomy in choosing their husbands, and have a more egalitarian relationship with their spouses. Furthermore, the study finds that educational homogamy and hypogamy afford greater autonomy to women. Even without a concomitant increase in labor force participation, college education among women appears to have a transformative effect on marriage in India. [ABSTRACT FROM AUTHOR]

Introduction: Data mining in healthcare is a nascent arena of research in healthcare. Heterogeneity of Diabetes Mellitus in terms of clinical presentation calls for newer methods of research to study potential risk factors. Aim: The paper aims to use clustering techniques to identify the relationship between the four variables, namely the pre-prandial and postprandial sugar level, age and sex. Methods: The data was taken from a diagnostic laboratory in Wagholi, Pune. We conducted K-mean algorithm, EM algorithm, model-based clustering and t-mixture model. Results: It is evidenced that the data was best fitted to the t-mixture model. Our 50% samples were people with diabetes, 17% had prediabetes. Trivial correlation existed between age and sugar level. Males and females were equally at risk of having diabetes. Data presented concludes that age and sex have no effect on the risk of having diabetes. Data mining can be used to deduce meaningful clusters to drive plan-based interventions in the population. Conclusion: Methods of data mining can be used to deduce meaningful clusters in a heterogeneous dataset thus providing policymakers and healthcare researchers with novel information that will potentially contribute in formulating evidence-based policies. [ABSTRACT FROM AUTHOR]