Nurses' and paediatricians' knowledge about infant sleeping positions and the risk of sudden infant death syndrome in Turkey

Objective: The purpose of this article is to determine nurses' and paediatricians' knowledge regarding the sleeping positions and environment of infants. Methods: This descriptive and cross-sectional study was conducted at 18 randomly selected hospitals in eight different cities in Turkey. There were 1,156 participants in the study (252 physicians and 904 nurses). The data were collected by means of a questionnaire form developed by the investigators. Data analysis included percentage and chi-square analyses. Results: Among the nurses, 74.1% were between the ages of 21 and 35, 32.0% had a bachelor's degree, 65.0% had job experience of six years or more. Among the paediatricians, 69.0% were between the ages of 21 and 35; 42.5% had job experience of six years or more. 88.8% of the nurses said that the mother should share same room with the infant but in a separate bed. Over two-thirds of the nurses said that a pillow should not be used when an infant was asleep, and 98.0% would not cover an infant's face. 86.5% of the paediatricians said that the mother could share same room with the infant but in a separate bed. 76.2% of the paediatricians said that they would not use a pillow when the infant were asleep, and 97.2% would not cover an infant's face. Most nurses and physicians responded that infants of 0-6 months of age slept on their sides during the daytime, in the nighttime, when left alone in a room, and after feeding. Conclusions: The results showed that physicians and nurses were not sufficiently knowledgeable about infant sleeping positions; however, they had sufficient knowledge about the risk factors for sudden infant death syndrome. The education of both nurses and physicians working in pediatric wards about the risk factors of SIDS may decrease SIDS deaths in Turkey

Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset

As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. Providing diagnostic aid for diabetes disease by using a set of data that contains only medical information obtained without advanced medical equipment, can help numbers of people who want to discover the disease or the risk of disease at an early stage. This can possibly make a huge positive impact on a lot of peoples lives. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use SOM, PCA and NN for clustering, noise removal and classification tasks, respectively. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction in relation to methods developed in the previous studies. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system