Rao A.A.,Jawaharlal Nehru Technological University Kakinada |
Rao Dattatreya A.V.,Acharya Nagarjuna University |
Sridhar G.R.,Endocrine and Diabetes Center
Journal of Computer Science | Year: 2010
Problem statement: The k-means method is one of the most widely used clustering techniques for various applications. However, the k-means often converges to local optimum and the result depends on the initial seeds. Inappropriate choice of initial seeds may yield poor results. kmeans++ is a way of initializing k-means by choosing initial seeds with specific probabilities. Due to the random selection of first seed and the minimum probable distance, the k-means++ also results different clusters in different runs in different number of iterations. Approach: In this study we proposed a method called Single Pass Seed Selection (SPSS) algorithm as modification to k-means++ to initialize first seed and probable distance for k-means++ based on the point which was close to more number of other points in the data set. Result: We evaluated its performance by applying on various datasets and compare with k-means++. The SPSS algorithm was a single pass algorithm yielding unique solution in less number of iterations when compared to k-means++. Experimental results on real data sets (4-60 dimensions, 27-10945 objects and 2-10 clusters) from UCI demonstrated the effectiveness of the SPSS in producing consistent clustering results. Conclusion: SPSS performed well on high dimensional data sets. Its efficiency increased with the increase of features in the data set; particularly when number of features greater than 10 we suggested the proposed method. © 2010 Science Publications.
Rao M.R.N.,Vignan Institute of Technology and Science |
Sridhar G.R.,Endocrine and Diabetes Center |
Madhu K.,Andhra University |
Rao A.A.,Jawaharlal Nehru Technological University Kakinada
Diabetes and Metabolic Syndrome: Clinical Research and Reviews | Year: 2010
Background: In diabetes mellitus, quality of life is recognized to be an integral outcome measure of management. We have developed a neural network system which is trained to predict the measurements of quality of life in diabetes, using data generated in real life. Methods: We developed a multi-layer perceptron neural network (NN) model, which had been trained by back propagation algorithm. Data was obtained from a cohort of 241 individuals with diabetes, which has been published. We used age, gender, weight, fasting plasma glucose as a set of inputs and predicted measures of quality of life (satisfaction, impact, social and diabetes worry). Results: Using the NN model, men reported significantly higher levels of satisfaction with the treatment being provided to them than women. Women had greater social and diabetes worry. The results have been considered based on the observation of the normalized system error (NSE) values of the neural network and are consistent with results obtained from traditional statistical methods. Conclusion: We have developed a prototype neural network model to measure the quality of life in diabetes, when biological or biographical variables are given as inputs. © 2008 Diabetes India.
Roze S.,HEVA HEOR |
Saunders R.,Ossian Health Economics and Communications |
Brandt A.-S.,Danmark A/S |
de Portu S.,Medtronic |
And 3 more authors.
Diabetic Medicine | Year: 2015
Aim: To evaluate the clinical benefits and cost-effectiveness of the sensor-augmented pump compared with self-monitoring of plasma glucose plus continuous subcutaneous insulin infusion in people with Type 1 diabetes. Methods: The CORE Diabetes Model was used to simulate disease progression in a cohort of people with baseline characteristics taken from a published meta-analysis. Direct and indirect costs for 2010-2011 were calculated from a societal payer perspective, with cost-effectiveness calculated over the patient's lifetime. Discount rates of 3% per annum were applied to the costs and the clinical outcomes. Results: Use of the sensor-augmented pump was associated with an increase in mean discounted, quality-adjusted life expectancy of 0.76 quality-adjusted life years compared with continuous subcutaneous insulin infusion (13.05 ± 0.12 quality-adjusted life years vs 12.29 ± 0.12 quality-adjusted life years, respectively). Undiscounted life expectancy increased by 1.03 years for the sensor-augmented pump compared with continuous subcutaneous insulin infusion. In addition, the onset of complications was delayed (by a mean of 1.15 years) with use of the sensor-augmented pump. This analysis resulted in an incremental cost-effectiveness ratio of 367,571 SEK per quality-adjusted life year gained with the sensor-augmented pump. The additional treatment costs related to the use of the sensor-augmented pump were partially offset by the savings attributable to the reduction in diabetes-related complications and the lower frequency of self-monitoring of plasma glucose. Conclusions: Analysis using the CORE Diabetes Model showed that improvements in glycaemic control associated with sensor-augmented pump use led to a reduced incidence of diabetes-related complications and a longer life expectancy. Use of the sensor-augmented pump was associated with an incremental cost-effectiveness ratio of 367,571 SEK per quality-adjusted life year gained, which is likely to represent good value for money in the treatment of Type 1 diabetes in Sweden. What's new?: This study builds on a recent meta-analysis to provide further insights into the clinical and safety aspects of real-time continuous glucose monitoring. Simulations of disease progression show how these aspects translate into long-term patient benefits and healthcare costs. Simulations indicate that continuous glucose monitoring leads to later onset and reduced incidence of acute and long-term diabetes-related complications. The findings may have an important influence on informing treatment choices with respect to providing value both for patients and healthcare payers. © 2015 The Authors. Diabetic Medicine © 2015 Diabetes UK.
Sridhar G.R.,Endocrine and Diabetes Center
Advances in Experimental Medicine and Biology | Year: 2010
Glutathione S-transferases (GST) belong to the transferase group of enzymes; GST are a family of enzymes that catalyze the addition of glutathione to endogenous or xenobiotic, often toxic electrophilic chemicals, and a major group of detoxification enzymes. We used the homology modeling technique to construct the structure of Gallus gallus GST. The amino acid sequence identity between the target protein and sequence of template protein 1ML6 (Mus musculus) was 66.2%. Based on the template structure, the protein model was constructed by using the Homology program Modeller9v1, and briefly refined by energy minimization steps; it was validated by PROCHECK. In all, 94.4% of the amino acids were in allowed regions of Ramachandran plot, showing the accuracy of the model and good stereochemical quality. Our results correlated well with the experimental data reported earlier, which proved the quality of the model. This generated model can be further used for the design and development of more potent GST inhibitors. © 2010 Springer Science+Business Media, LLC.
Sridhar G.R.,Endocrine and Diabetes Center |
Putcha V.,AccoStats Solutions |
Lakshmi G.,Kasturba Medical College
Journal of Association of Physicians of India | Year: 2010
Objective: To assess the time trends in the prevalence of diabetes at our Centre from 1994-2004 (N: 19,072 individuals) on the following parameters: age group, sex, rural or urban area and individuals with freshly diagnosed diabetes versus known diabetes Study Design and Setting: Analysis of data from electronic medical records at a referral Endocrine and Diabetes Centre in Southern India Methods: We have employed the period prevalence method and person-time risk to express the results. The concept of person-time risk can be estimated as the actual time-at-risk in years that all persons contributed to a study. The person-time can be estimated for each patient when a patient changed from diabetic free to diabetic patient. This can be captured for each patient from the variable onset of first diagnoses as a diabetic patient. Thus person-time is employed to derive information on the rate at which people acquire the disease. Results: Between 1994 and 2004 however there is an increasing trend in the number of individuals in the young, particularly the 18-34 year group. Similarly there is a steadily increasing pattern in both urban and rural areas; the number from rural areas tended to increase compared to urban areas. The number of women with diabetes tended to increase over the 10-year period Conclusion: Between 1994 and 2004 among persons with diabetes who presented at our Centre, there was a trend toward more number of younger persons, particularly women from rural areas. © JAPI.