Important Role In Medical Science Research Articles

Advanced Ensemble Framework for Diabetes Outcome Forecasting

Abstract: Diabetes is one of the chronic diseases, which is increasing from year to year. Developing an automated system that can detect diabetes patients plays an important role in medical science. A stack classifier model is designed for the detection of diabetes by combining three base estimators such as Random Forest Classifier, LightGBM Classifier, and K-Nearest Neighbors Classifier, and Logistic Regression as meta-classifiers. The data preprocessing includes transforming categorical variables into numerical format. Each base learner is trained on the preprocessed data and predictions are made. In the meta learner stage, Logistic Regression is trained to make predictions based on the predictions of the base learners. The goal of the meta learner is to learn how to combine the predictions of the base learners to make a more accurate final prediction on whether the patient is diabetic or not. The use of a stacking classifier improves prediction accuracy compared to using a single classifier. The developed model gives an accuracy of 98%. The 5-fold cross validation is used to get a more robust estimation of generalization error. Thus, the developed model offers a means to enhance early detection and elevate the quality of care for diabetic patients.

Fingerprint based authentication framework for IoT environment

<p>Internet of Things (IoT) approach makes the humans life easy and safe and due to its acceptability and portability. Humans are surrounded by multitude of heterogeneous devices which assist them in their daily routine. The integration of Blockchain technology with IoT played an important role in medical science and online transaction. At same point of time, the IoT network and devices are vulnerable that can be exploit to affect the network and devices security and accessibility. Therefore, robust authentication techniques should be implementing to secure the IoT network as well as devices accessibility. Due to high heterogeneity of applications, providing authentication for these devices is always challengeable. In this work, a novel fingerprint-based authentication framework in implemented in which Gaussian filter is used for image smoothness, Robert filter for edge detection and achieve 94% accuracy while authentication.</p>

Diabetes Prediction Using Machine Learning Algorithms and Ontology

Diabetes is one of the chronic diseases, which is increasing from year to year. The problems begin when diabetes is not detected at an early phase and diagnosed properly at the appropriate time. Different machine learning techniques, as well as ontology-based ML techniques, have recently played an important role in medical science by developing an automated system that can detect diabetes patients. This paper provides a comparative study and review of the most popular machine learning techniques and ontology-based Machine Learning classification. Various types of classification algorithms were considered namely: SVM, KNN, ANN, Naive Bayes, Logistic regression, and Decision Tree. The results are evaluated based on performance metrics like Recall, Accuracy, Precision, and F-Measure that are derived from the confusion matrix. The experimental results showed that the best accuracy goes for ontology classifiers and SVM.

Brain tumor identification and tracking using image processing technique

Abnormal growth of mass or cell in the brain is considered as a brain tumor. The proper functioning of the brain is affected by the growth of any tissue or cell abnormally. Knowing about the abnormalities in a specific area, an effective technology-based system is required which can provide accurate and meaningful information about the disorders. Now a day's image processing plays an important role in medical science as well as the field is also developing in many ways. Various methods in image processing help doctors to get maximum information about the disease with minimum possible errors. Some of these techniques are CT scans (Computed Tomography Scan), Magnetic resonance imaging (MRI), X-rays. These advanced technologies are capable of detecting all the biggest to smallest defects in the body. All these computerized techniques are equipped with high resolution and better quality image readers. This research investigates brain tumor identification and tracking using an image processing technique. For this research 40, sample images were examined using image processing techniques using classification methods with brain tumor problem and without brain tumor problems investigated. The present work represents 92.5% accuracy for the detection of patients with brain tumors during analysis.

Automated Facial Expression Recognition Framework Using Deep Learning.

Facial expression is one of the most significant elements which can tell us about the mental state of any person. A human can convey approximately 55% of information nonverbally and the remaining almost 45% through verbal communication. Automatic facial expression recognition is presently one of the most difficult tasks in the computer science field. Applications of facial expression recognition (FER) are not just limited to understanding human behavior and monitoring person's mood and the mental state of humans. It is also penetrating into other fields such as criminology, holographic, smart healthcare systems, security systems, education, robotics, entertainment, and stress detection. Currently, facial expressions are playing an important role in medical sciences, particularly helping the patients with bipolar disease, whose mood changes very frequently. In this study, an algorithm, automated framework for facial detection using a convolutional neural network (FD-CNN) is proposed with four convolution layers and two hidden layers to improve accuracy. An extended Cohn-Kanade (CK+) dataset is used that includes facial images of different males and females with expressions such as anger, fear, disgust, contempt, neutral, happy, sad, and surprise. In this study, FD-CNN is performed in three major steps that include preprocessing, feature extraction, and classification. By using this proposed method, an accuracy of 94% is obtained in FER. In order to validate the proposed algorithm, K-fold cross-validation is performed. After validation, sensitivity and specificity are calculated which are 94.02% and 99.14%, respectively. Furthermore, the f1 score, recall, and precision are calculated to validate the quality of the model which is 84.07%, 78.22%, and 94.09%, respectively.

Convolutional Neural Networks in Medical Image Understanding

In the era of social media images/pictures play a vital role. Facebook, whatsapp, instagram everywhere we see a lot of pictures nowadays. Along with social media, the pictures play a very important role in medical science. Medical Image can help in diagnosis, clinical treatment and teaching tasks. Traditional classification of images has reached an end because of its time taking nature and efforts made to extract, select and classify . This problem is solved with the help of CNN(Convolutional neural network).In medical science we have treatment for body anomalies that were not there before .Using the deep learning models of CNN we can detect the disease like Cancer ,Lung Infection and treat it. This article aims to provide a comprehensive survey of applications of CNNs in medical image understanding.

Predictive Mechanism for Medicines Availability in Government Health Centers

During the peak time of a disease, some medicines are not available in the hospital. Now-a-days, medicines play an important role in medical science. To treat a patient there are absence of medications in government emergency clinics. Our fundamental point of our venture is to build up a Healthcare Information framework to give prescient examination on Medicines accessibility in Government clinics. In view of patient inflow for a specific affliction or ailment, authentic information and current information, framework could produce a report on what all medications ought to be accessible in the clinic. On expanding the productivity of the emergency clinic by overseeing accessibility of medicines utilizing machine learning algorithm (regression technique). This encourages government emergency clinics to follow the medicines accessibility of a specific occasional infection.

Does academic interest play a more important role in medical sciences than in other disciplines? A nationwide cross-sectional study in China

BackgroundResearch examining the effects of academic interest on students learning achievement across various disciplines, especially a comparison of the effects of academic interest between medical sciences and other disciplines, is still scarce. This study addressed this gap by answering ‘does academic interest play a more important role in medical sciences than in other disciplines?’.MethodsA retrospective cross-sectional study, based on a large project of the National Undergraduate Student Development Survey (NUSDS) conducted by the Ministry of Education of China and Peking University in 2014, was designed to explore the role of academic interest in medical sciences and other disciplines. The participants were resampled to better represent the national distribution of undergraduate students in terms of their demographic characteristics. Specifically, survey data from 54,398 undergraduate students from 87 Chinese universities and colleges were used to address our research questions. We then used the propensity score matching (PSM) model to estimate the effect of academic interest on academic achievement and to compare the effects across different disciplines.ResultsAcademic interest had a significant positive impact on academic performance, with an effect size of 2.545 (p = 0.000). Specifically, the effect sizes for the disciplines of medical sciences, humanities, social sciences, natural sciences and engineering were 2.310 (p = 0.000), 2.231 (p = 0.000), 2.016 (p = 0.000), 3.840 (p = 0.000) and 2.698 (p = 0.000), respectively. The results show that no particular academic interest in medical sciences is needed to achieve academic success when compared with natural sciences and engineering programmes, but success in medical sciences requires more academic interest than success in humanities or social sciences.ConclusionsThis study clarifies the effect of academic interest on undergraduates’ academic achievement while controlling for their demographic characteristics and family factors. The results provide insights into the role of academic interest in academic performance across various disciplines and can inform the college admissions practices of both institutions and high school students in China.

OCT despeckling via weighted nuclear norm constrained non-local low-rank representation

As a non-invasive imaging modality, optical coherence tomography (OCT) plays an important role in medical sciences. However, OCT images are always corrupted by speckle noise, which can mask image features and pose significant challenges for medical analysis. In this work, we propose an OCT despeckling method by using non-local, low-rank representation with weighted nuclear norm constraint. Unlike previous non-local low-rank representation based OCT despeckling methods, we first generate a guidance image to improve the non-local group patches selection quality, then a low-rank optimization model with a weighted nuclear norm constraint is formulated to process the selected group patches. The corrupted probability of each pixel is also integrated into the model as a weight to regularize the representation error term. Note that each single patch might belong to several groups, hence different estimates of each patch are aggregated to obtain its final despeckled result. Both qualitative and quantitative experimental results on real OCT images show the superior performance of the proposed method compared with other state-of-the-art speckle removal techniques.

Properties of skin stem cells and their potential clinical applications in modern dermatology.

Stem cells play an important role in medical science, and scientists are investing large sums in order to perform sophisticated studies designed to establish potential clinical applications of stem cells. Growing experience has enabled researchers to determine the precise nature of stem cell division. Although the properties of this particular population of cells have been known and used for some time, mainly with regards to bone marrow-derived mesenchymal stem cell transplantation, we now face a significant challenge in implementing the practical use of skin-derived precursors, making it possible to avoid the necessity for patients to undergo invasive procedures in order to obtain stem cells from bone marrow. Multiple trials have so far been performed, bringing hope for the treatment of disorders previously considered untreatable. Patients suffering from a number of dermatological diseases, including malignant melanoma, systemic lupus erythematosus, vitiligo, alopecia or junctional epidermolysis bullosa, may benefit from treatment based on stem cells. The aim of this review is to summarize available data on stem cells and their potential applications in the treatment of dermatological disorders. The work described is based on data published up to the end of September 2016.

Clinical Research: Good News Coming from Ca2+/cAMP Signaling Interaction

Clinical research plays an important role in medical sciences by determining the safety and efficiency of medications, devices and diagnostic products for human use. Our discovery of the interactions between intracellular signaling pathways as mediated by Ca2+ and cAMP (Ca2+/cAMP signaling interaction) in neurotransmission could help improve clinical research. This discovery emerged from classical neurotransmission studies using rodent vas deferens as a model. In addition, the concept of Ca2+-dependent muscle contraction (Ca2+ influx triggers muscle contraction) is amply accepted. Thus, Ca2+ channel blockers (CCB) should reduce muscle contraction. Nonetheless, using this model, some studies performed since 1975 reported that reduction of Ca2+ influx by low concentrations of CCB (verapamil, diltiazem or nifedipine) produced a paradoxical increase of the contractions mediated sympathetic nerves, a phenomenon known as “calcium paradox”. Recent studies using adrenal chromaffin cells have also demonstrated that CCB caused a paradoxical increase of the catecholamine release. Because these compounds are blocking the L-type voltage-activated Ca2+ channels (VACC), an augmented nerve-mediated response due to increased neurotransmitter release was an unexpected outcome. In 2013, we revealed that the Ca2+/cAMP signaling interaction could properly explain the so-called “calcium paradox”. The original paper published by us in Cell Calcium (2013) has appeared four times in ScienceDirect TOP 25 Hottest Articles lists. In conclusion, these findings may significantly impact on cardiovascular and neurodegenerative diseases, cancer, stem cell-based therapies and many other diseases, and may stimulate the development of new pharmacological strategies to combat these diseases.

Brain Stroke Segmentation using Fuzzy C-Means Clustering

Image processing technique plays an important role in medical science for envisage various phenomenal structure of human body. Even though it helps more, sometimes it’s very difficult to detect abnormal structures of human body by using simple images. Magnetic Resonance Imaging (MRI) is the one of the most significant technique to analyze human body and helpful for distinguishing and expounding the neural architecture of human brain effectively. This proposed strategy focus on detection and extraction of brain stroke from different patient’s MRI images. In this work some preprocessing techniques like noise removal, filtering and segmentation is used for extract brain stroke partition accurately. The segmentation of brain stroke is implemented by using Fuzzy C-Means (FCM) clustering with two different levels of extraction. Edge detection is used for finding segmented portion of brain stroke edges accurately. Finally the stroke size is calculated for help doctors to make effective decisions about brain stroke. The experimental result proven that the proposed method is successful in detecting and extraction brain stroke efficiently with less time.

349 POSTER Global index to summarise health risks versus benefit in the ATAC (‘Arimidex’, Tamoxifen, Alone or in Combination) trial

Study has been carried out on filtration of bacteria having different shapes and sizes by using particle track microfilters. The filtration of spherical shaped bacteria (staphylococci), rod-like bacteria (colon bacilli) and mixed miscellaneous bacteria living in pool water in countryside shows quantitatively that particle track microfilters are a reliable tool to remove various bacteria from liquid substances. The study convinces us that in future particle track microfilters will play an important role in medical sciences and daily life.

Manipulation and effectiveness of bacteria filtration with particle track microfilters

Abstract Study has been carried out on filtration of bacteria having different shapes and sizes by using particle track microfilters. The filtration of spherical shaped bacteria (staphylococci), rod-like bacteria (colon bacilli) and mixed miscellaneous bacteria living in pool water in countryside shows quantitatively that particle track microfilters are a reliable tool to remove various bacteria from liquid substances. The study convinces us that in future particle track microfilters will play an important role in medical sciences and daily life.