Machine Learning Techniques for Corneal Diseases Diagnosis: A Survey

2020 ◽  
pp. 2150016
Author(s):  
Samer Kais Jameel ◽  
Sezgin Aydin ◽  
Nebras H. Ghaeb
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Data ◽  
Medical Images ◽  
Diagnostic Techniques ◽  
Machine Learning Techniques ◽  
Corneal Disease ◽  
Automated Learning ◽  
Learning Techniques ◽  
The Way

Machine learning techniques become more related to medical researches by using medical images as a dataset. It is categorized and analyzed for ultimate effectiveness in diagnosis or decision-making for diseases. Machine learning techniques have been exploited in numerous researches related to corneal diseases, contribution to ophthalmologists for diagnosing the diseases and comprehending the way automated learning techniques act. Nevertheless, confusion still exists in the type of data used, whether it is images, data extracted from images or clinical data, the course reliant on the type of device for obtaining them. In this study, the researches that used machine learning were reviewed and classified in terms of the kind of utilized machine for capturing data, along with the latest updates in sophisticated approaches for corneal disease diagnostic techniques.

The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision‐making

2018 ◽  
Vol 31 (3) ◽  
pp. 429-435 ◽  
Author(s):  
Kathryn Rendell ◽  
Irena Koprinska ◽  
Andre Kyme ◽  
Anja A Ebker‐White ◽  
Michael M Dinh
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Decision making via semi-supervised machine learning techniques

2016 ◽  
Author(s):  
Ευτύχιος Πρωτοπαπαδάκης
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques

Ο όρος μάθηση με μερική επίβλεψη αναφέρεται σε ένα ευρύ πεδίο τεχνικών μηχανικής μάθησης, οι οποίες χρησιμοποιούν τα μη τιτλοφορημένα δεδομένα για να εξάγουν επιπλέον ωφέλιμη πληροφορία. Η μερική επίβλεψη αντιμετωπίζει προβλήματα που σχετίζονται με την επεξεργασία και την αξιοποίηση μεγάλου όγκου δεδομένων και τα όποια κόστη σχετίζονται με αυτά (π.χ. χρόνος επεξεργασίας, ανθρώπινα λάθη). Απώτερος σκοπός είναι η ασφαλή εξαγωγή συμπερασμάτων, κανόνων ή προτάσεων. Τα μοντέλα λήψης απόφασης που χρησιμοποιούν τεχνικές μερικής μάθησης έχουν ποικίλα πλεονεκτήματα. Σε πρώτη φάση, χρειάζονται μικρό πλήθος τιτλοφορημένων δεδομένων για την αρχικοποίηση τους. Στη συνέχεια, τα νέα δεδομένα που θα εμφανιστούν αξιοποιούνται και τροποποιούν κατάλληλα το μοντέλο. Ως εκ τούτου, έχουμε ένα συνεχώς εξελισσόμενο μοντέλο λήψης αποφάσεων, με την ελάχιστη δυνατή προσπάθεια.Τεχνικές που προσαρμόζονται εύκολα και οικονομικά είναι οι κατεξοχήν κατάλληλες για τον έλεγχο συστημάτων, στα οποία παρατηρούνται συχνές αλλαγές στον τρόπο λειτουργίας. Ενδεικτικά πεδία εφαρμογής εφαρμογής ευέλικτων συστημάτων υποστήριξης λήψης αποφάσεων με μερική μάθηση είναι: η επίβλεψη γραμμών παραγωγής, η επιτήρηση θαλάσσιων συνόρων, η φροντίδα ηλικιωμένων, η εκτίμηση χρηματοπιστωτικού κινδύνου, ο έλεγχος για δομικές ατέλειες και η διαφύλαξη της πολιτιστικής κληρονομιάς.

Predicting Diabetes Mellitus With Machine Learning Techniques Using Multi-Criteria Decision Making

2021 ◽  
Vol 11 (2) ◽  
pp. 38-52
Author(s):  
Abhinav Juneja ◽  
Sapna Juneja ◽  
Sehajpreet Kaur ◽  
Vivek Kumar
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Healthy Lifestyle ◽  
Age Groups ◽  
Machine Learning Techniques ◽  
Multi Criteria Decision Making ◽  
Learning Techniques ◽  
Supervised Learning Algorithms ◽  
Unsupervised Algorithms ◽  
Early Diagnosis Of Diabetes

Diabetes has become one of the common health issues in people of all age groups. The disease is responsible for many difficulties in lifestyle and is represented by imbalance in hyperglycemia. If kept untreated, diabetes can raise the chance of heart attack, diabetic nephropathy, and other disorders. Early diagnosis of diabetes helps to maintain a healthy lifestyle. Machine learning is a capability of machine to learn from past pattern and occurrences and converge with experience to optimise and give decision. In the current research, the authors have employed machine learning techniques and used multi-criteria decision-making approach in Pima Indian diabetes dataset. To classify the patients, they examined several different supervised and unsupervised predictive models. After detailed analysis, it has been observed that the supervised learning algorithms outweigh the unsupervised algorithms due to the output class being a nominal classified domain.

Primary Factors Influencing the Decision to Vaccinate Against COVID-19 in the United States: A Predictive Analytics Approach (Preprint)

2021 ◽  
Author(s):  
Serkan Varol ◽  
Serkan Catma ◽  
Diana Reindl ◽  
Elizabeth Serieux
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Predictive Analytics ◽  
The United States ◽  
Machine Learning Techniques ◽  
Vaccine Hesitancy ◽  
Willingness To Accept ◽  
Perceptions And Attitudes ◽  
Learning Techniques ◽  
Vaccine Decision Making

BACKGROUND Vaccine refusal still poses a risk to reaching herd immunity in the United States. The existing literature focuses on identifying the predictors that would impact the willingness to accept (WTA) vaccines using survey data. These variables range from the socio-demographic characteristics of the participants to the perceptions and attitudes towards the vaccines so each variable’s statistical relationship with the WTA a vaccine can be investigated. However, while the results of these studies may have important implications for understanding vaccine hesitancy by offering interpretation of the statistical relationships, the prediction of vaccine decision-making has rarely been investigated OBJECTIVE We aimed to identify the factors that contribute to the prediction of COVID-19 vaccine acceptors and refusers using machine learning METHODS A nationwide survey was administered online in November, 2020 to assess American public perceptions and attitudes towards COVID-19 vaccines. Seven machine learning techniques were utilized to identify the model with the highest predictive power. Moreover, a set of variables that would contribute the most to the predictions of vaccine acceptors and refusers was identified using Gini importance based on Random Forest structure RESULTS The resulting machine learning algorithm has better prediction ability for willingness to accept (82%) versus reject (51%) a COVID-19 vaccine. In terms of predictive success, the Random Forest model outperformed the other machine learning techniques with a 69.52% accuracy rate. Worrying about (re) contracting Covid 19 and opinions regarding mandatory face covering were identified as the most important predictors of vaccine decision-making CONCLUSIONS The complexity of vaccine hesitancy needs to be investigated thoroughly before the threshold needed to reach population immunity can be achieved. Predictive analytics can help the public health officials design and deliver individually tailored vaccination programs that would increase the overall vaccine uptake.

scholarly journals Morphology and Interaction of Galaxies using Deep Learning

2016 ◽  
Vol 12 (S325) ◽  
pp. 205-208
Author(s):  
Fernando Caro ◽  
Marc Huertas-Company ◽  
Guillermo Cabrera
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Visual Inspection ◽  
Interaction Analysis ◽  
Machine Learning Techniques ◽  
Huge Amount ◽  
New Approaches ◽  
Learning Techniques ◽  
First Galaxies ◽  
The Way

AbstractIn order to understand how galaxies form and evolve, the measurement of the parameters related to their morphologies and also to the way they interact is one of the most relevant requirements. Due to the huge amount of data that is generated by surveys, the morphological and interaction analysis of galaxies can no longer rely on visual inspection. For dealing with such issue, new approaches based on machine learning techniques have been proposed in the last years with the aim of automating the classification process. We tested Deep Learning using images of galaxies obtained from CANDELS to study the accuracy achieved by this tool considering two different frameworks. In the first, galaxies were classified in terms of their shapes considering five morphological categories, while in the second, the way in which galaxies interact was employed for defining other five categories. The results achieved in both cases are compared and discussed.

scholarly journals Bike Sharing Prediction using Deep Neural Networks

2017 ◽  
Vol 1 (3) ◽  
pp. 83 ◽  
Author(s):  
Chandrasegar Thirumalai ◽  
Ravisankar Koppuravuri
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Neural Nets ◽  
Machine Learning Techniques ◽  
Business Decisions ◽  
Learning Techniques ◽  
Bike Sharing ◽  
The Way

In this paper, we will use deep neural networks for predicting the bike sharing usage based on previous years usage data. We will use because deep neural nets for getting higher accuracy. Deep neural nets are quite different from other machine learning techniques; here we can add many numbers of hidden layers to improve the accuracy of our prediction and the model can be trained in the way we want such that we can achieve the results we want. Nowadays many AI experts will say that deep learning is the best AI technique available now and we can achieve some unbelievable results using this technique. Now we will use that technique to predict bike sharing usage of a rental company to make sure they can take good business decisions based on previous years data.

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