scholarly journals Bayesian Kernel Methods

2021 ◽  
Vol 6 (1) ◽  
pp. 26-39
Author(s):  
Arti Saxena ◽  
Vijay Kumar
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Medical Decision Making ◽  
Kernel Method ◽  
Machine Learning Techniques ◽  
Medical Decision ◽  
Learning Tools ◽  
Meaningful Information ◽  
Learning Techniques ◽  
Available Information

In the healthcare industry, sources look after different customers with diverse diseases and complications. Thus, at the source, a great amount of data in all aspects like status of the patients, behaviour of the diseases, etc. are collected, and now it becomes the job of the practitioner at source to use the available data for diagnosing the diseases accurately and then prescribe the relevant treatment. Machine learning techniques are useful to deal with large datasets, with an aim to produce meaningful information from the raw information for the purpose of decision making. The inharmonious behavior of the data is the motivation behind the development of new tools and demonstrates the available information to some meaningful information for decision making. As per the literature, healthcare of patients can be analyzed through machine learning tools, and henceforth, in the article, a Bayesian kernel method for medical decision-making problems has been discussed, which suits the purpose of researchers in the enhancement of their research in the domain of medical decision making.

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 Intersections of machine learning and epidemiological methods for health services research

2020 ◽  
Author(s):  
Sherri Rose
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Services Research ◽  
Health Services ◽  
Machine Learning Techniques ◽  
Epidemiological Methods ◽  
Learning Tools ◽  
Services Research ◽  
Learning Techniques ◽  
Regression Techniques

Abstract The field of health services research is broad and seeks to answer questions about the health care system. It is inherently interdisciplinary, and epidemiologists have made crucial contributions. Parametric regression techniques remain standard practice in health services research with machine learning techniques currently having low penetrance in comparison. However, studies in several prominent areas, including health care spending, outcomes and quality, have begun deploying machine learning tools for these applications. Nevertheless, major advances in epidemiological methods are also as yet underleveraged in health services research. This article summarizes the current state of machine learning in key areas of health services research, and discusses important future directions at the intersection of machine learning and epidemiological methods for health services research.

scholarly journals Preparedness and Mitigation by projecting the risk against COVID-19 transmission using Machine Learning Techniques

2020 ◽  
Author(s):  
Akshay Kumar ◽  
Farhan Mohammad Khan ◽  
Rajiv Gupta ◽  
Harish Puppala
Keyword(s):  
Machine Learning ◽  
Gaussian Process Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Learning Tools ◽  
Learning Techniques ◽  
Health Organization ◽  
Respiratory Coronavirus ◽  
Criticality Index

AbstractThe outbreak of COVID-19 is first identified in China, which later spread to various parts of the globe and was pronounced pandemic by the World Health Organization (WHO). The disease of transmissible person-to-person pneumonia caused by the extreme acute respiratory coronavirus 2 syndrome (SARS-COV-2, also known as COVID-19), has sparked a global warning. Thermal screening, quarantining, and later lockdown were methods employed by various nations to contain the spread of the virus. Though exercising various possible plans to contain the spread help in mitigating the effect of COVID-19, projecting the rise and preparing to face the crisis would help in minimizing the effect. In the scenario, this study attempts to use Machine Learning tools to forecast the possible rise in the number of cases by considering the data of daily new cases. To capture the uncertainty, three different techniques: (i) Decision Tree algorithm, (ii) Support Vector Machine algorithm, and (iii) Gaussian process regression are used to project the data and capture the possible deviation. Based on the projection of new cases, recovered cases, deceased cases, medical facilities, population density, number of tests conducted, and facilities of services, are considered to define the criticality index (CI). CI is used to classify all the districts of the country in the regions of high risk, low risk, and moderate risk. An online dashpot is created, which updates the data on daily bases for the next four weeks. The prospective suggestions of this study would aid in planning the strategies to apply the lockdown/ any other plan for any country, which can take other parameters to define the CI.

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