Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks in the medical discipline emerged for data-driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system, and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or (...) not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes dataset using accuracy and mean squared error as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP-SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Cultural Context of Post-traumatic Stress DisorderCarolyn Smith-Morris (bio)Keywordspost-traumatic stress disorder (PTSD), culture, medical anthropology, fight-or-flight responseIn his Clinical Anecdote, Dr. Christopher Bailey gamely imagines the evolutionary underpinnings of his patient's distressing lack of war wounds. As part of a careful and engaged discussion of care for his suffering patient, Dr. Bailey suggests that our evolved fight-or-flight response to the alarms of the African savannah may be at work (...) in Colin's distress. The fight-or-flight response is something of great interest to anthropologists, who rely on Darwin's theory of evolution to explain much in human experience. According to Dr. Bailey's application of the theory, Colin's visceral response to threat is protective in the presence of danger, but destructive (particularly to his mental health) in the absence of such challenges. So Dr. Bailey considers whether we may have an evolved “need” for threat, and that Colin's trauma is in part related to the lack of such threat in his wartime experience.According to Darwin's ideas, evolved traits may be adaptive but may also simply be random appearances that have not yet produced cause for evolutionary eradication. The fight-or-flight response, or any of the hypothalamic–pituitary–adrenal axis events initiated under either acute or chronic stress, is therefore occurring in Colin's reactions to everyday frustrations, to wartime exposures, and even during the stressful nontrauma of feeling “wimpy.” Dr. Bailey's idea about an evolved need for threat is tautological. That need is not biological, but cultural.Although post-traumatic stress disorder is medically real, it is an event that develops in a particular place and time. Likewise, gender roles that demand scar battles and trophies of a certain form are culturally determined. Culture is central to this anecdote: a culture-specific occurrence of post-traumatic stress disorder and culturally driven gender roles for men, especially warriors. Stress is indeed present in Colin's life: our postindustrial culture's separation from everyday death and violence make episodes like war and combat all the more shocking or traumatic; the lack of social recognition for his service also seems to be one of Colin's stressors. Both the shock of warfare and the trauma of his social invisibility are as vivid to his evolutionarily informed body as is the presence of a crouching lioness in the room. So it is the host of cultural expectations, and his inability to mobilize social and cognitive resources to respond to them, that is wounding Colin. Talk therapy should draw out these cultural details [End Page 235] and, as Dr. Bailey has already begun to do, emplot Colin into a more heroic (his)story of his service to our nation.Related ArticlesFeature Article: Clinical Anecdotes: A Painful Lack of WoundsCommentary: The Nature of Proof in PsychiatryCommentary: Truth and Healing a Veteran's DepressionCommentary: Inappropriate RegretCommentary: The Cultural Context of Post-traumatic Stress DisorderCommentary: Explication or Explanation?Commentary: The Importance of What Psychiatrists Care AboutCommentary: Mourning or MelancholiaResponse: A Painful Lack of ConnectionCarolyn Smith-Morris Carolyn Smith-Morris is an Associate Professor in the Department of Anthropology at Southern Methodist University in Dallas, Texas. Her work focuses on chronic disease and the health impacts of culture change and acculturative stress in North America. She is engaged in several research programs among migrating Mexican workers, among poor African-Americans in Dallas, and for the Gila (pronounced Hee-la) River Indian Community of Southern Arizona, a tribe that suffers from the highest recorded rates of type 2 diabetes in the world. She also holds a research fellowship with the Foundation for Community Empowerment, serves on the Board of the Society for Medical Anthropology, and is a fellow in the Society for Applied Anthropology. She published Diabetes Among the Pima in 2006 and has several articles detailing her applied research on diabetes, reproduction, health care ethics, and end-of-life care. She can be contacted via e-mail at: csmorris@mail.smu.eduReferenceBailey, C. 2009. A painful lack of wounds. Philosophy, Psychiatry, & Psychology 16, no. 3:223–224. [End Page... (shrink)

. This paper illustrates the applications of various ensemble methods for enhanced classification accuracy. The case in point is the Pima Indian Diabetic Dataset. The computational model comprises of two stages. In the first stage, k-means clustering is employed to identify and eliminate wrongly classified instances. In the second stage, a fine tuning in the classification was effected. To do this, ensemble methods such as AdaBoost, bagging, dagging, stacking, decorate, rotation forest, random subspace, MultiBoost and grading were invoked along (...) with five chosen base classifiers, namely support vector machine, radial basis function network, decision tree J48, naïve Bayes and Bayesian network. The k-fold cross validation technique is adopted. Computational experiments with the proposed method showed an improvement of 16.14% to 22.49% in the classification accuracy compared to literature survey. Among the ensemble methods tried, MultiBoost ensemble with SVM classifier and grading ensemble with naïve Bayes showed the best performance followed by MultiBoost, stacking and grading ensemble with Bayesian classifier, rotation forest ensemble with RBF and grading and rotation forest ensemble with J48. This investigation conclusively proves the significance of cascading k-means clustering with ensemble methods in the enhanced accuracy in categorization of diabetic dataset. (shrink)