Prediction of diabetes disease using an ensemble of machine learning multi-classifier models.

Background And Objective: Diabetes is a life-threatening chronic disease with a growing global prevalence, necessitating early diagnosis and treatment to prevent severe complications. Machine learning has emerged as a promising approach for diabetes diagnosis, but challenges such as limited labeled data, frequent missing values, and dataset imbalance hinder the development of accurate prediction models. Therefore, a novel framework is required to address these challenges and improve performance.

The effect of breastfeeding on children's growth indices up to 6 months: An application of multivariate t linear mixed model.

Background: This study aimed to determine the effect of breastfeeding on children's growth indices.

Materials And Methods: Longitudinal data of children's growth (height, weight, and head circumference) were as a dependent variable and type of nutrition as an independent variable with using multivariate t linear mixed model.

Results: The indicated that the height, weight, and head circumference of infants who were fed with breast milk showed a statistically significant difference ( < 0.

Developing a bi-objective resilience relief logistic considering operational and disruption risks: a post-earthquake case study in Iran.

Today, according to the occurrence of numerous disasters in allover over the world, designing the proper and comprehensive plan for relief logistics has received a lot of attention from crisis managers and people. Besides, considering resilience capability along with operational and disruption risks leads to the robustness of the humanitarian relief chain (HRC), and this comprehensive framework ensures the essential supplies delivery to the beneficiaries and is close to real-world problems. The resilience parameters used for the second objective are obtained by a strong Best Worst Method (BWM).

Occupational Exposure and Health Impairments of Formaldehyde on Employees of a Wood Industry.

Background: Occupational exposure to formaldehyde may decrease white blood cell counts and change blood concentration. In this study, the influences of occupational exposure to formaldehyde on the number of white blood cells and blood concentrations were studied.

Methods: This case-control study was conducted in June of 2012 at North Wood Factory, Golestan Province, Iran.