Search

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]

Objectives: The present study was designed with the objective to study the prevalence of depression and anxiety symptoms among adolescents and to explore the association of these disorders with Body Mass Index. Settings and Design: School setting and Cross-Sectional study design. Participants: 546 adolescents, aged 13-15 years, studying in public schools (n=5) in Delhi (Purposive sampling). Methods and Material: Anthropometric measurements: Height of the subjects was measured using a stadiometer in cm (to nearest 0.5cm). Weight was assessed using TANITA's Body Fat Monitor (UM-076). After gathering height and weight data, BMI (kg/m2) Z scores were calculated and classified into four categories: Underweight, Normal weight, Overweight and Obesity based on WHO's growth standards for BMI for age cutoffs. Mental Health Assessment: For this purpose, we used the Child Behavior Checklist (CBCL) for ages 6-18 years; parent report version. Results: A V shaped curve (trend) was observed between the association of depression and anxiety scores in four BMI categories; with majority of the malnourished adolescents having higher scores for depression and anxiety symptoms. It was interesting to note that any deviation from normal weight, either underweight or overweight/obese was significantly associated with depression (p = <0.001) and anxiety (p = <0.001) scores. Conclusions: The study emphasizes the association between body weight and depression and anxiety symptoms among adolescents studying in public schools of Delhi. It adds to the growing body of research in the area of nutritional psychiatry which needs to be promoted for the prevention and management of these disorders through a healthy diet. [ABSTRACT FROM AUTHOR]