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Background: Mass media significantly contributes to the dissemination of scientific information, including health information on prevention, diagnosis, treatment, national programs, and government schemes. Aims and objectives: To identify the number of news articles related to health in 4 regional newspapers and to classify the news items in terms of type of disease/ health issue covered. Methodology: Four leading newspapers were reviewed to identify the health-related news, their importance in terms of location in the newspaper, and the type of health issue covered. These were further classified based on the level of evidence used. Results: We extracted 720 health-related articles; 411 in English and 309 in Kannada dailies. The English newspaper had maximum coverage on environment 95 (23.1%) and health advisories 60 (14.5%). The Kannada papers on the other hand were dominated by health advisories 65 (21%), followed by nutrition and fitness 48 (15.3%), and environment 40 (12.9%). After the articles were categorized according to their evidence, it was discovered that 92% of them were evidence-based, with 84% of those articles based on Grade-II evidence. Conclusion: Health news in newspapers varies, often focusing on environment and health advisories, and relies heavily on expert opinion rather than empirical data. [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]