A Classification System for Diabetic Patients with Machine Learning Techniques

International audienceDiabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM

A New Similarity Measure Based on Mean Measure of Divergence for Collaborative Filtering in Sparse Environment

AbstractMemory based algorithms, often referred to as similarity based Collaborative Filtering (CF) is one of the most popular and successful approaches to provide service recommendations. It provides automated and personalized suggestions to consumers to select variety of products. Typically, the core of similarity based CF which greatly affect the performance of recommendation system is to finding similar users to a target user. Conventional similarity measures like Cosine, Pearson correlation coefficient, Jaccard similarity suffer from accuracy problem under sparse environment. Hence in this paper, we propose a new similarity approach based on Mean Measure of Divergence that takes rating habits of a user into account. The quality of recommendation of proposed approach is analyzed on benchmark datasets: ML 100K, ML-1M and Each Movie for various sparsity levels. The results depict that the proposed similarity measure outperforms existing measures in terms of prediction accuracy

ORGANIZATIONAL INNOVATIVENESS MEASUREMENT: INCORPORATING CSI AND CFI IN THE DEVELOPMENT OF A DIAGNOSTIC TOOL

Organizational innovativeness is the latent capability of an organization that produces innovation over time. This is like any other abilities of an organization such as producing goods, services and thus it can be influenced, improved and increased with proper focus and deliberation. The role of innovativeness in the survival of business is essential and unavoidable and yet the findings and constructs in the field of organizational innovativeness are fuzzy and inconsistent. To address this need, this research begins with two research questions: what is the current state of innovativeness measurement in technology companies? And How can a diagnostic tool help to ensure growth and success for technology companies? In conjunction with three objectives: identify and present a set of critical success indicators (CSIs) and critical failure indicators (CFIs) for technology companies to be innovative, determine how innovative technology companies position themselves to ensure growth and success in the marketplace, and develop a tool that can be adopted by technology companies to measure their innovativeness successfully. The quest to close this research gap and provide a comprehensive diagnostic tool, the research proposes a framework that combines critical success indicator (CSI) and critical failure indicator (CFI) into the same framework to diagnose organizational innovativeness. This framework consists of five dimensions: culture, leadership, strategy, structure, and execution. And synthesizes a set of CSIs and CFIs for each dimension. This research applies mixed method research. The empirical data were collected from focus group study, semi structured interview and survey. The results from the empirical study suggests that pursuing critical success indicators do not necessarily result in higher levels of organizational innovativeness. Rather, it is the pursuit of both critical success indicators and critical failure indicators that help organizations in enhancing their overall organizational innovativeness level. This study proposed a diagnostic tool that a business can implement to assess its organizational innovativeness continuously and devise improvement plans based on the current outcome. A simple and intuitive visualization matrix created in this research helps a business management team to draw conclusions and gain insights into innovation dynamics of an organization

Mass transfer from a torus shaped body

A mathematical model for molecular diffusion in a torus was derived. Handlos and Baron have assumed a system of tori in their eddy diffusion model for liquid-liquid extraction from droplets. However, the effect of torus curvature was neglected in their studies. Since they assumed the torus could be represented by an infinite cylinder. In this study, the effect of torus curvature was considered on the concentration profile and the fraction of the solute extracted. The partial differential equation describing the model consists of three independent variables, and a finite difference technique was employed for the solution of the mathematical model. It was found in this work that the concentration profiles within a torus differed from those in an infinite cylinder. However, it was also found that the fraction of solute extracted in a torus was nearly identical to that predicted using the solution for an infinite cylinder. Since the effect of the torus curvature is negligible, the solution for an infinite cylinder may be used for diffusion to a torus --Abstract, page x

Biochemical characterization of ArsI: a novel C-As lyase for degradation of environmental organoarsenicals

Organoarsenicals such as methylarsenical methylarsenate (MAs(V)) and aromatic arsenicals including roxarsone (4-hydroxy-3-nitrophenylarsenate or Rox(V)) have been extensively used as an herbicide and growth enhancers in animal husbandry, respectively. They undergo environmental degradation to more toxic inorganic arsenite (As(III)) that contaminates crops and drinking water. We previously identified a bacterial gene (arsI) responsible for aerobic MAs(III) demethylation. The gene product, ArsI, is a Fe(II)-dependent extradiol dioxygenase that cleaves the carbon-arsenic (C-As) bond in MAs(III) and trivalent aromatic arsenicals. The objective of this study was to elucidate the ArsI mechanism. Using isothermal titration calorimetry, we determined the dissociation constants (Kd) and ligand-to-protein stoichiometries (N) of ArsI for Fe(II), MAs(III) and aromatic phenyl arsenite. Using a combination of methods including chemical modification, site-directed mutagenesis, and fluorescent spectroscopy, we demonstrated that amino acid residues predicted to participate in Fe(II)-binding (His5-His62-Glu115) and substrate binding (Cys96-Cys97) are all involved in catalysis. Finally, the products of Rox(III) degradation were identified as As(III) and 4-hydroxy-2-nitrophenol, demonstrating that ArsI is a dioxygenase that incorporates one oxygen atom from dioxygen into the carbon and the other to the arsenic to catalyze the cleavage of the C-As bond. These results augment our understanding of the mechanism of this novel C-As lyase

The Versatility of Epidemiology: Association of Chronic Diseases to Age-Related Hearing Loss and the Risk of Cancer Within a Community Exposed to Gasoline

This dissertation demonstrates the versatility of epidemiology in public health research. The association between hearing sensitivity and diabetes, cardiovascular disease (CVD), and their risk factors was examined in a population of 2,049 adults within the Health, Aging, and Body Composition Study (mean age 77.5 ± 2.8 years; 37% black). CVD and diabetes may contribute to age-related hearing loss by affecting blood flow within the inner ear via macro- and micro-vascular changes. Clinical CVD was not associated with hearing sensitivity however; subclinical CVD measures were moderately associated with poorer auditory function in females. After controlling for age, race, and site, CVD risk factors positively associated with worse mid-frequency hearing thresholds in males were weight, insulin, glucose, triglycerides, and smoking and in females were heart rate and glucose. Risk factors associated with worse high frequency thresholds were weight, insulin, triglycerides, and smoking in males and heart rate, glucose, and smoking in females. Diabetes was associated with mid-frequency hearing loss upon adjustment for common hearing loss risk factors (OR=1.60; 95%CI: 1.26-2.02). The metabolic syndrome was associated with mid-frequency hearing loss in whites prior to excluding diabetics. These results suggest that diabetes, in conjunction with CVD, contributes to age-related hearing loss, particularly strial presbycusis, and independent of common hearing loss risk factors. Given the high prevalence of hearing impairment among older adults, the identification of potentially modifiable risk factors for age-related hearing loss is of public health significance. Epidemiology can also be utilized in more applied settings. A retrospective cohort study was conducted to determine if residents affected by an underground gasoline spill in Hazle Township/Hazleton, Pennsylvania were at increased risk for cancer from 1990-2000. A total of 663 individuals representing 275 households comprised the study population. Age-adjusted standard incidence ratios (SIRs) were calculated using Pennsylvania rates to determine expected numbers. The age-adjusted leukemia SIR for the gasoline affected area was 4.40 (95%CI: 1.09-10.24). These results suggest a possible association between chronic low-level benzene exposure and increased risk for leukemia in the residents living near the spill site. This project directly impacted the public health of residents and also demonstrated the importance of collaboration and surveillance

FILTERED-DYNAMIC-INVERSION CONTROL FOR UNKNOWN MINIMUM-PHASE SYSTEMS WITH UNKNOWN RELATIVE DEGREE

We present filtered-dynamic-inversion (FDI) control for unknown linear time-invariant systems that are multi-input multi-output and minimum phase with unknown-but-bounded relative degree. This FDI controller requires limited model information, specifically, knowledge of an upper bound on the relative degree and knowledge of the first nonzero Markov parameter. The FDI controller is a single-parameter high-parameter-stabilizing controller that is robust to uncertainty in the relative degree. We characterize the stability of the closed-loop system. We present numerical examples, where the FDI controller is implemented in feedback with mathematical and physical systems. The numerical examples demonstrate that the FDI controller for unknown relative degree is effective for stabilization, command following, and disturbance rejection. We demonstrate that for a sufficiently large parameter, the average power of the closed-loop performance is arbitrarily small