As type 2 diabetes becomes more prevalent across the globe, predicting its sources becomes more important. However, there is a big void in predicting the risk factors of this disease. Thus, the purpose of this study is to predict diabetes risk factors by applying machine learning (ML) algorithms. Two-fold feature selection techniques (i.e., principal component analysis, PCA, and information gain, IG) have been applied to boost the prediction accuracy. Then, the optimal features are fed into five ML algorithms, namely decision tree, random forest, support vector machine, logistic regression, and KNN. The primary data used to train the ML model were collected based on the safety procedure described in the Helsinki Declaration, 2013, and 738 records were included in the final analysis. The result has shown an accuracy level of over 82.2%, with an AUC (area under the ROC curve) value of 87.2%. This research not only identified the most important clinical and nonclinical factors in diabetes prediction, but it also found that the clinical risk factor (glucose) is the most relevant for diabetes prediction, followed by dietary factors. The noteworthy contribution of this research is the identification of previously unclassified factors left over from the previous study that considered both clinical and non-clinical aspects.

This study aims to investigate the impact of socio-demographic factors on health in Bangladesh using the World Health Survey dataset. Using the seemingly unrelated regression models the study reveals that female compared to male faces more challenges in terms of problems regarding mobility, self-care, bodily pain, sleeping, remembering and depression. The study also finds that as the level of education increases the severity of facing these problems reduces. Furthermore, people who are never married widowed and separated or divorced experience more problems in terms of self-care, remembrance, sleeping as well as depression compared to those who are currently married.

Leadership is crucial for achieving any development and goal for any country. This paper aimed to develop the cycle through which the power is circulating. In this paper, the word viscus stands for a hidden core fact. The Viscus cycle is defined as the circulating factors which use to achieve vision and goal of come in ruling power of a country through good, bad, or even worse action. The paper answers questions on South Asian countries those practicing democratic rule and killing leaders to come in rule by use the viscus cycle of power practice. An extensive literature review was conducted to understand the reciprocation of the verdict course and power. Out of south Asian countries, India, Bangladesh, Bhutan, Nepal, and Pakistan were deeply reviewed. These five countries were the countries of study. The standard and regular phenomena were identified from which the viscus cycle made.  The cycle contained four major boxes with four distinguished colors.  The Centre red is for power and superpower, the yellow color is for invisible force, the grey is for prominent stakeholders or community, and the green stands for shadow power. Philosophical assessment of the public domain revealed that power is circulating within the mentioned four (superpower, invisible strength, distinguished community, and shadow power) fact to date. Considering the paper as review work, the paper concludes with a recommendation that this is the time countries of the world need to work on continuous self-assessment of leaders regarding their guidance and order to their workers (and supporters) that will assist in preventing violent, death, and war.