<span lang="EN-US">Diabetes has been acknowledged as a well-known risk factor for renal and cardiovascular disorders, cardiac stroke and leads to a lot of morbidity in the society. Reducing the disease prevalence in the community will provide substantial benefits to the community and lessen the burden on the public health care system. So far, to detect the disease innumerable data mining approaches have been used. These days, incorporation of machine learning is conducive for the construction of a faster, accurate and reliable model. Several methods based on ensemble classifiers are being used by researchers for the prediction of diabetes. The proposed framework of prediction of diabetes mellitus employs an approach called stacking based ensemble using non-dominated sorting genetic algorithm (NSGA-II) scheme. The primary objective of the work is to develop a more accurate prediction model that reduces the lead time i.e., the time between the onset of diabetes and clinical diagnosis. Proposed NSGA-II stacking approach has been compared with Boosting, Bagging, Random Forest and Random Subspace method. The performance of Stacking approach has eclipsed the other conventional ensemble methods. It has been noted that k-nearest neighbors (KNN) gives a better performance over decision tree as a stacking combiner.</span>

Background: Seroprevalence of COVID-19 antibody production in a person can be dependent on many physiological and demographic aspects such as previous infection, age, sex, body mass index, and also status of vaccination. It is of immense value to know about demographic aspect of COVID-19 antibody production so as to know about vulnerable population and suggest preventive measures. Aims and Objectives: The present study was aimed to determine effect of demographic variables COVID-19 antibody production in population of urban area. Materials and Methods: In this study, a total of 2454 subjects were screened for COVID-19 neutralizing antibody by ELISA technique. Subjects more than 18-year-old were selected for the study. We used cluster sampling method for data collection. A pre-structured questionnaire was administered after informed consent and 5 mL venous blood was collected in plain bulb for testing. Results: The prevalence of neutralizing antibody was found to be 93.9%. Female had 95% positive antibodies against males (92.34%). Maximum positive antibody status was seen in age group of 20–40 (55.6%). About 77.9% subjects following mixed diet were having positive COVID-19 antibody test as compared to subjects following pure vegetarian (10.2%). About 83.2% subjects who received vaccine showed positive antibody test. The lowest positivity is seen in underweight subjects (8%) followed by obese subjects (12.7%). Maximum inhibition % was seen in subjects using Vitamin C Zinc tablets (92.1%). The lowest inhibition was seen in subjects using Unani Kadha. A one-way ANOVA revealed that there was not a statistically significant difference in prophylactic measures for prevention of COVID-19 infection other than vaccination and COVID-19 neutralizing antibody inhibition %. ([F-1.363], P=0.244). Conclusion: COVID-19 neutralizing antibody prevalence was found to be much higher in the population (96%), which was mostly associated with younger age, gender, diet, and vaccination status of the population. Extensive studies are required to establish any association between prophylactic methods other than vaccination and COVID-19 antibody response.