Various Feature Selection Techniques in Type 2 Diabetic Patients for the Prediction of Cardiovascular Disease

Cardiovascular disease (CVD) is a serious but preventable complication of type 2 diabetes mellitus (T2DM) that results in substantial disease burden, increased health services use, and higher risk of premature mortality [10]. People with diabetes are also at a greatly increased risk of cardiovascular which results in sudden death, which increases year by year. Data mining is the search for relationships and global patterns that exist in large databases but are `hidden' among the vast amount of data, such as a relationship between patient data and their medical diagnosis. Usually medical databases of type 2 diabetic patients are high dimensional in nature. If a training dataset contains irrelevant and redundant features (i.e., attributes), classification analysis may produce less accurate results. In order for data mining algorithms to perform efficiently and effectively on high-dimensional data, it is imperative to remove irrelevant and redundant features. Feature selection is one of the important and frequently used data preprocessing techniques for data mining applications in medicine. Many of the research area in data mining has improved the predictive accuracy of the classifiers by applying the various techniques of feature selection This paper illustrates, the application of feature selection technique in medical databases, will enable to find small number of informative features leading to potential improvement in medical diagnosis. It is proposed to find an optimal feature subset of the PIMA Indian Diabetes Dataset using Artificial Bee Colony technique with Differential Evolution, Symmetrical Uncertainty Attribute set Evaluator and Fast Correlation-Based Filter (FCBF). Then Mutual information based feature selection is done by introducing normalized mutual information feature selection (NMIFS). And valid classes of input features are selected by applying Hybrid Fuzzy C Means algorithm (HFCM)

Diagnosing Heart Diseases For Type 2 Diabetic Patients By Cascading The Data Mining Techniques

Motivated by the world-wide increasing mortality of heart disease patients each year, researchers have been using data mining techniques to help health care professionals in the diagnosis of heart disease. Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. To review the primary prevention studies that focused on the development, validation and impact assessment of a heart disease risk model, scores or rules that can be applied to patients with type 2 diabetes. Efficient predictive modeling is required for medical researchers and practitioners. Attribute values measurement using entropy and information gain parameters. This study proposes Hybrid type 2 diabetes Prediction Model which uses Improved Fuzzy C Means (IFCM) clustering algorithm aimed at validating chosen class label of given data in which incorrectly classified instances are removed and. pattern extracted from original data. Support Vector Machine (SVM) algorithm is used to build the final classifier model by using the k-fold cross-validation method. The aim of this paper is to highlight all the techniques and risk factors that are considered for diagnosis of heart disease. This paper will provide a roadmap for researchers seeking to understand existing automated diagnosis of heart disease

Opportunities of E - Learning Adapting Mobile and Cloud Computing Techniques

This study mainly focused on the application of cloud computing and mobile computing in the E-learning environment. The popularity of learning of the internet, the construction of perfect web-based learning environment has become one of the hot points on researching remote education. Cloud computing is growing rapidly, with applications in almost any area, including education. E-Learning systems usually require many hardware and software resources. There are many education institutions that cannot afford such investments, and cloud computing is the best solution. This paper presents the benefits of E-Learning based on mobile computing and learning with mobile application using cloud environment and the benefits in several sectors, especially in the area of learning. The performance and features are evaluated that can expect from the use of cloud based application on a mobile device, and the effects it will have on the device that runs

Restricting Barrier and Finding the Shortest Path in Wireless Sensor Network Using Mobile Sink

Wireless Sensor Network (WSN) is a collection of spatially deployed in wireless sensors. In general, sensing field could contain various barriers which cause loss of information transferring towards the destination. As a remedy, this proposed work presents an energy-efficient routing mechanism based on cluster in mobile sink. The scope of this work is to provide a mobile sink in a single mobile node which begins data-gathering from starting stage, then immediately collects facts from cluster heads in single-hop range and subsequently, it returns to the starting stage. During the movement of the mobile sink if the barrier exists in the sensing field it can be detected using Spanning graph and Grid based techniques. The possible locations for the mobile sink movement can be reduced easily by Spanning graph. At last, Barrier avoidance-shortest route was established for mobile sink using Dijkstra algorithm. The Distributed location information is collected using a Timer Bloom Filter Aggregation (TBFA) scheme. In the TBFA scheme, the location information of Mobile node (MNs) is maintained by Bloom filters by each Mobile agent (MA). Since the propagation of the whole Bloom filter for every Mobile node (MN) movement leads to high signaling overhead, each Mobile agent (MA) only propagates changed indexes in the Bloom filter when a pre-defined timer expires. To verify the performance of the TBFA scheme, an analytical model is developed on the signaling overhead and the latency and devise an algorithm to select an appropriate timer value. Extensive simulation and Network Simulator 2(NS2) results are given to show the accuracy of analytical models and effectiveness of the proposed method

Micropropagation and conservation of selected endangered anticancer medicinal plants from the Western Ghats of India

Globally, cancer is a constant battle which severely affects the human population. The major limitations of the anticancer drugs are the deleterious side effects on the quality of life. Plants play a vital role in curing many diseases with minimal or no side effects. Phytocompounds derived from various medicinal plants serve as the best source of drugs to treat cancer. The global demand for phytomedicines is mostly reached by the medicinal herbs from the tropical nations of the world even though many plant species are threatened with extinction. India is one of the mega diverse countries of the world due to its ecological habitats, latitudinal variation, and diverse climatic range. Western Ghats of India is one of the most important depositories of endemic herbs. It is found along the stretch of south western part of India and constitutes rain forest with more than 4000 diverse medicinal plant species. In recent times, many of these therapeutically valued herbs have become endangered and are being included under the red-listed plant category in this region. Due to a sharp rise in the demand for plant-based products, this rich collection is diminishing at an alarming rate that eventually triggered dangerous to biodiversity. Thus, conservation of the endangered medicinal plants has become a matter of importance. The conservation by using only in situ approaches may not be sufficient enough to safeguard such a huge bio-resource of endangered medicinal plants. Hence, the use of biotechnological methods would be vital to complement the ex vitro protection programs and help to reestablish endangered plant species. In this backdrop, the key tools of biotechnology that could assist plant conservation were developed in terms of in vitro regeneration, seed banking, DNA storage, pollen storage, germplasm storage, gene bank (field gene banking), tissue bank, and cryopreservation. In this chapter, an attempt has been made to critically review major endangered medicinal plants that possess anticancer compounds and their conservation aspects by integrating various biotechnological tool

Characterization of a Drosophila Alzheimer's Disease Model: Pharmacological Rescue of Cognitive Defects

Transgenic models of Alzheimer's disease (AD) have made significant contributions to our understanding of AD pathogenesis, and are useful tools in the development of potential therapeutics. The fruit fly, Drosophila melanogaster, provides a genetically tractable, powerful system to study the biochemical, genetic, environmental, and behavioral aspects of complex human diseases, including AD. In an effort to model AD, we over-expressed human APP and BACE genes in the Drosophila central nervous system. Biochemical, neuroanatomical, and behavioral analyses indicate that these flies exhibit aspects of clinical AD neuropathology and symptomology. These include the generation of Aβ40 and Aβ42, the presence of amyloid aggregates, dramatic neuroanatomical changes, defects in motor reflex behavior, and defects in memory. In addition, these flies exhibit external morphological abnormalities. Treatment with a γ-secretase inhibitor suppressed these phenotypes. Further, all of these phenotypes are present within the first few days of adult fly life. Taken together these data demonstrate that this transgenic AD model can serve as a powerful tool for the identification of AD therapeutic interventions

Peptide-modified nanoparticles inhibit formation of Porphyromonas gingivalis biofilms with Streptococcus gordonii

Paridhi Kalia,1 Ankita Jain,1 Ranjith Radha Krishnan,1 Donald R Demuth,1,2 Jill M Steinbach-Rankins2–5 1Department of Oral Immunology and Infectious Diseases, University of Louisville School of Dentistry, 2Department of Microbiology and Immunology, University of Louisville School of Medicine, 3Department of Bioengineering, University of Louisville Speed School of Engineering, 4Department of Pharmacology and Toxicology, University of Louisville School of Medicine, 5Center for Predictive Medicine, University of Louisville, Louisville, KY, USA Purpose: The interaction of Porphyromonas gingivalis with commensal streptococci promotes P. gingivalis colonization of the oral cavity. We previously showed that a synthetic peptide (BAR) derived from Streptococcus gordonii potently inhibited the formation of P. gingivalis/S. gordonii biofilms (IC50 =1.3 µM) and reduced P. gingivalis virulence in a mouse model of periodontitis. Thus, BAR represents a novel therapeutic to control periodontitis by limiting P. gingivalis colonization of the oral cavity. Here, we sought to develop drug-delivery vehicles for potential use in the oral cavity that comprise BAR-modified poly(lactic-co-glycolic)acid (PLGA) nanoparticles (NPs). Methods: PLGA-NPs were initially modified with palmitylated avidin and subsequently conjugated with biotinylated BAR. The extent of BAR modification was quantified using a fluorescent-labeled peptide. Inhibition of P. gingivalis adherence to S. gordonii by BAR-modified NPs was compared with free peptide using a two-species biofilm model. Results: BAR-modified NPs exhibited an average size of 99±29 nm and a more positive surface charge than unmodified NPs (zeta potentials of -7 mV and -25 mV, respectively). Binding saturation occurred when 37 nmol BAR/mg of avidin-NPs was used, which resulted in a payload of 7.42 nmol BAR/mg NPs. BAR-modified NPs bound to P. gingivalis in a dose-dependent manner and more potently inhibited P. gingivalis/S. gordonii adherence and biofilm formation relative to an equimolar amount of free peptide (IC50 of 0.2 µM versus 1.3 µM). BAR-modified NPs also disrupted the preformed P. gingivalis/S. gordonii biofilms more effectively than free peptide. Finally, we demonstrate that BAR-modified NPs promoted multivalent association with P. gingivalis, providing an explanation for the increased effectiveness of NPs. Conclusion: These results indicate that BAR-modified NPs deliver a higher local dose of peptide and may represent a more effective therapeutic approach to limit P. gingivalis colonization of the oral cavity compared to treatment with formulations of free peptide. Keywords: nanoparticle, peptide delivery, multivalent, drug delivery, Porphyromonas gingivalis, periodontal disease&nbsp