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

Assessing baseline glucocorticoids as conservation biomarkers in a declining aerial insectivore

Conservation biologists are increasingly incorporating a diversity of integrative approaches to monitor, manage, and mitigate the growing threats to biodiversity imparted by climate change and other anthropogenic pressures. Over the past 15 years, stress hormones (i.e., glucocorticoids: corticosterone and cortisol) have been gaining considerable attention as sensitive physiological biomarkers of wildlife disturbance. However, despite a substantial accumulation of studies citing glucocorticoids (GCs) as potential indicators of condition, health, or disturbance, comparatively little is known about their actual utility for conservation monitoring. This thesis aims to validate three key characteristics of baseline plasma GCs that are necessary to their employment as sensitive, predictive biomarkers of wildlife disturbance: 1) correlation with environmental quality; 2) consistency across individuals in response to environmental alteration; 3) relationship with fitness metrics at the individual and population level. I complete these validations across two different reproductive stages in female tree swallows (Tachycineta bicolor), a member of the aerial insectivore guild of birds that is in population decline in North America. My results indicate that baseline GCs may not reflect the natural variation in components of the internal and extrinsic environment that are associated with habitat quality or disturbance. In addition, baseline GCs show considerable within-individual variation across the breeding season, and display individually-specific responses to an experimentally-induced change in environmental quality (i.e., a decline in foraging profitability). Further, baseline GC levels do not relate to multiple metrics of fitness (offspring quality, reproductive output, or survival) despite the careful control of potentially confounding contexts such as age, reproductive stage, time of day, and body condition. Finally, at the average level, my results indicate that an environmental perturbation (i.e., a decline in foraging profitability) can have consequences for body condition, behaviour, and current and future baseline GC levels in habitat type-specific ways without concomitant influences on fitness. Collectively, my findings suggest that baseline GCs may not be easily interpretable as individual or population-level indicators of disturbance or fitness. Importantly, these results indicate that GCs cannot be assumed to represent conservation biomarkers across species or time periods without careful validation