scholarly journals Dr Miner: An Application of Auto Detecting Diabetic Retinopathy using Auto Colour Correlogramand Bagging

2021 ◽  
Vol 12 (3) ◽  
pp. 1916-1922
Author(s):  
Chew-Wai Yap Et.al
Keyword(s):  
Diabetic Retinopathy ◽  
Data Science ◽  
Learning Algorithms ◽  
Clinical Excellence ◽  
Fundus Images ◽  
Data Set ◽  
Detection Model ◽  
Nearest Neighbours ◽  
Using Data ◽  
The Uk

An application of auto-detecting Diabetic Retinopathy (DR) is indispensable to aid the ophthalmologists in diagnosing patients and also to help relevant organisations in accumulating and analysing data. This project presents DR Miner, an application that can extract data from fundus images, identify the symptoms of DR in retina images by using data science approaches, and collect the ophthalmologist’s review to improve the detection model in the future. To form the DR data set with binary classes, Auto Colour Correlogram (ACC) was utilised to extract the features from DR images. Over-sampling was then conducted to balance the class distribution in the data set. To reduce the variance of the single learning algorithms, we evaluated various bagging approaches. Theresults showed that the bagging approaches gave better results than the single learning algorithms in general. Out of all bagging approaches we evaluated, bagged k-nearest neighbours gave the best result. The sensitivity achieved was 85.1%, which met the requirement set by the UK National Institute for Clinical Excellence.

A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics

2021 ◽  
pp. 416-428
Author(s):  
R. Suganya ◽  
Rajaram S. ◽  
Kameswari M.
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Data Science ◽  
Learning Algorithms ◽  
Research Literature ◽  
Machine Learning Algorithms ◽  
Thyroid Disorder ◽  
Classification Models ◽  
Indian Women ◽  
Using Data

Currently, thyroid disorders are more common and widespread among women worldwide. In India, seven out of ten women are suffering from thyroid problems. Various research literature studies predict that about 35% of Indian women are examined with prevalent goiter. It is very necessary to take preventive measures at its early stages, otherwise it causes infertility problem among women. The recent review discusses various analytics models that are used to handle different types of thyroid problems in women. This chapter is planned to analyze and compare different classification models, both machine learning algorithms and deep leaning algorithms, to classify different thyroid problems. Literature from both machine learning and deep learning algorithms is considered. This literature review on thyroid problems will help to analyze the reason and characteristics of thyroid disorder. The dataset used to build and to validate the algorithms was provided by UCI machine learning repository.

Automatic Detection of Diabetic Retinopathy with Statistical Method and Bayesian Classifier

2020 ◽  
Vol 10 (5) ◽  
pp. 1225-1233 ◽  
Author(s):  
Yafen Kang ◽  
Ying Fang ◽  
Xiaobo Lai
Keyword(s):  
Diabetic Retinopathy ◽  
Statistical Method ◽  
Texture Features ◽  
Automatic Detection ◽  
Bayesian Classifier ◽  
Gray Level ◽  
Data Set ◽  
Naive Bayesian ◽  
Detection Model ◽  
Naïve Bayesian

Currently, the underlying medical conditions in China lag behind those in urban areas. There are some problems such as lack of resources of primary ophthalmologists and insufficient fundus image of diabetic retinopathy (DR) with markers. To solve the above questions, an automated detection model of diabetic retinopathy based on the statistical method and Naïve Bayesian (NB) classifier is proposed in this paper. Firstly, three sets of texture features are extracted, which are gray-level co-occurrence matrix texture features, different statistical texture features, and gray-level run-length matrix texture features. Secondly, the extracted texture features are used as input of the Naïve Bayesian classifier to classify the fundus images of diabetic retinopathy into three categories. The proposed automatic detection model for diabetic retinopathy is validated by a data set consisting of 568 images from China diabetic retinopathy screening project. The positive predictive accuracy of the system is 93.44%, the sensitivity and specificity are 91.94% and 88.24%, respectively.

Analysis and visualization of COVID-19 discourse on Twitter using data science: a case study of the USA, the UK and India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Haider Ilyas ◽  
Ahmed Anwar ◽  
Ussama Yaqub ◽  
Zamil Alzamil ◽  
Deniz Appelbaum
Keyword(s):  
Data Science ◽  
Supervised Machine Learning ◽  
Content Type ◽  
The Public ◽  
Public Sentiment ◽  
The People ◽  
The Usa ◽  
Using Data ◽  
The Uk

Purpose This paper aims to understand, examine and interpret the main concerns and emotions of the people regarding COVID-19 pandemic in the UK, the USA and India using Data Science measures. Design/methodology/approach This study implements unsupervised and supervised machine learning methods, i.e. topic modeling and sentiment analysis on Twitter data for extracting the topics of discussion and calculating public sentiment. Findings Governments and policymakers remained the focus of public discussion on Twitter during the first three months of the pandemic. Overall, public sentiment toward the pandemic remained neutral except for the USA. Originality/value This paper proposes a Data Science-based approach to better understand the public topics of concern during the COVID-19 pandemic.

scholarly journals Assessing the educational performance of different Brazilian school cycles using data science methods

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248525
Author(s):  
Joyce de Souza Zanirato Maia ◽  
Ana Paula Arantes Bueno ◽  
João Ricardo Sato
Keyword(s):  
School Performance ◽  
Data Science ◽  
School Structure ◽  
Science Methods ◽  
Data Set ◽  
Good School ◽  
Using Data ◽  
The Impact ◽  
Educational Indicators

Educational indicators are metrics that assist in assessing the quality of the educational system. They are often associated with economic and social factors suggested to contribute to good school performance, however there is no consensus on the impact of these factors. The main objective of this work was to evaluate the factors related to school performance. Using a data set composed by Brazilian schools’ performance (IDEB), socioeconomic and school structure variables, we generated different models. The non-linear model predicted the best performance, measured by the error and determination coefficient metrics. The heterogeneity of the importance of the variable between school cycles and regions of the country was detected, this effect may contribute to the development of public educational policies.

scholarly journals Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration

2018 ◽  
Vol 376 (2128) ◽  
pp. 20170357 ◽  
Author(s):  
Slava Jankin Mikhaylov ◽  
Marc Esteve ◽  
Averill Campion
Keyword(s):  
Artificial Intelligence ◽  
Public Sector ◽  
Data Science ◽  
Grand Challenge ◽  
Public And Private ◽  
The Public ◽  
Using Data ◽  
The Uk ◽  
High Uncertainty

Public sector organizations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high-uncertainty environments. The long-term success of data science and artificial intelligence (AI) in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and the public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities for and challenges of AI for the public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations.This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations’.

scholarly journals Performing Examination on H1B Visa using Data Analytics Techniques to Enhance the Employability Skills

2019 ◽  
Vol 9 (1) ◽  
pp. 7506-7509
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Employability Skills ◽  
Complete Analysis ◽  
Visualization Technique ◽  
Data Set ◽  
Foreign Labour ◽  
Data Engineering ◽  
Using Data ◽  
Job Postings

Data is useless without the skill to analyse it. Technology professional’s expertise in Data engineering are in high demand. The number of job postings related to Analytics has increased substantially. This paper provides a complete analysis on the H1B visa applicants. The analysis is based on the job positions, number of petitions filed by industry every year, demanding jobs with hike salary etc. The data set has been collected from The Office of Foreign Labour Certification (OFLC), the department responsible for issuing H1B. The Data visualization technique is used mainly to perform the analysis with respect to various parameters. This visualisation is done with the help of base map, a library in python for data science. The analysis report will provide a better enhancement in providing employability on skill based.

scholarly journals Early Detection of Diabetic Retinopathy by Using Deep Learning Neural Network

2018 ◽  
Vol 7 (4.11) ◽  
pp. 198 ◽  
Author(s):  
Mohamad Hazim Johari ◽  
Hasliza Abu Hassan ◽  
Ahmad Ihsan Mohd Yassin ◽  
Nooritawati Md Tahir ◽  
Azlee Zabidi ◽  
...  
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Confusion Matrix ◽  
Fundus Images ◽  
Training Set ◽  
Data Set ◽  
Neural Learning ◽  
Deep Learning Neural Network ◽  
Testing Set

This project presents a method to detect diabetic retinopathy on the fundus images by using deep learning neural network. Alexnet Convolution Neural Network (CNN) has been used in the project to ease the process of neural learning. The data set used were retrieved from MESSIDOR database and it contains 1200 pieces of fundus images. The images were filtered based on the project needed.  There were 580 pieces of images types .tif has been used after filtered and those pictures were divided into 2, which is Exudates images and Normal images. On the training and testing session, the 580 mixed of exudates and normal fundus images were divided into 2 sets which is training set and testing set. The result of the training and testing set were merged into a confusion matrix. The result for this project shows that the accuracy of the CNN for training and testing set was 99.3% and 88.3% respectively.   

scholarly journals Weakly Supervised Sensitive Heatmap framework to classify and localize diabetic retinopathy lesions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammed Al-Mukhtar ◽  
Ameer Hussein Morad ◽  
Mustafa Albadri ◽  
MD Samiul Islam
Keyword(s):  
Diabetic Retinopathy ◽  
Supervised Learning ◽  
Automatic Detection ◽  
Training Data ◽  
Optic Disk ◽  
Test Accuracy ◽  
Fundus Images ◽  
Data Set ◽  
Weakly Supervised ◽  
The Impact

AbstractVision loss happens due to diabetic retinopathy (DR) in severe stages. Thus, an automatic detection method applied to diagnose DR in an earlier phase may help medical doctors to make better decisions. DR is considered one of the main risks, leading to blindness. Computer-Aided Diagnosis systems play an essential role in detecting features in fundus images. Fundus images may include blood vessels, exudates, micro-aneurysm, hemorrhages, and neovascularization. In this paper, our model combines automatic detection for the diabetic retinopathy classification with localization methods depending on weakly-supervised learning. The model has four stages; in stage one, various preprocessing techniques are applied to smooth the data set. In stage two, the network had gotten deeply to the optic disk segment for eliminating any exudate's false prediction because the exudates had the same color pixel as the optic disk. In stage three, the network is fed through training data to classify each label. Finally, the layers of the convolution neural network are re-edited, and used to localize the impact of DR on the patient's eye. The framework tackles the matching technique between two essential concepts where the classification problem depends on the supervised learning method. While the localization problem was obtained by the weakly supervised method. An additional layer known as weakly supervised sensitive heat map (WSSH) was added to detect the ROI of the lesion at a test accuracy of 98.65%, while comparing with Class Activation Map that involved weakly supervised technology achieved 0.954. The main purpose is to learn a representation that collect the central localization of discriminative features in a retina image. CNN-WSSH model is able to highlight decisive features in a single forward pass for getting the best detection of lesions.

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