A hybrid super ensemble learning model for the early-stage prediction of diabetes risk.

Diabetes mellitus has become a rapidly growing chronic health problem worldwide. There has been a noticeable increase in diabetes cases in the last two decades. Recent advances in ensemble machine learning methods play an important role in the early detection of diabetes mellitus.

The effect of motivational interview persons with diabetes on self-management and metabolic variables.

Objective: To evaluate the effect of motivational interviewing on diabetics related to perceived self-management and metabolic variables.

Methods: The randomised controlled and experimental study was conducted at a public hospital located in the city of Gaziantep in Turkey and comprised diabetes patients attending the outpatient clinic of endocrinology and metabolic diseases from April to July 2015. The subjects were randomised into two equal intervention and control groups.

The Challenge of Diabetes in the Elderly and Affecting Factors: A Turkish Study.

This study was conducted with the goal of evaluating the challenge of diabetes in elderly individuals and affecting factors. This descriptive study was conducted with 88 diabetic patients 65 years of age or older who were admitted to the endocrinology and metabolism disorders outpatient clinic data of the study collected by using a questionnaire prepared by the researchers and the Elderly Diabetes Burden Scale. Total score of the scale varies between 19 and 92 points.