Machine learning algorithms implementation into embedded systems with web application user interface

2017 ◽  
Author(s):  
Kamil Zidek ◽  
Jan Pitel ◽  
Alexander Hosovsky
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Embedded Systems ◽  
Web Application ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Heart disease prediction using Advanced Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 2160-2163
Author(s):  
Minal Shahakar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Web Application ◽  
Intelligent System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Prediction ◽  
End User ◽  
Data Mining Techniques

It might have happened so many times that you or someone yours need doctors help immediately, but they are not available due to some reason. The Heart Disease Prediction application is an end user support to the online. Here, we propose a web application that allows users to get instant guidance on their heart disease through an intelligent system online. The application is fed with various details and the heart disease associated with those details. The applications allows user to share their heart related issues. It then processes user specific details to check for various illnesses that could be associated with it. Here we use some intelligent data mining techniques to the most accurate that could be associated with patient‟s details. Based on result, system automatically shows the result specific doctors for further treatment and the system allows user to view doctor‟s details.

scholarly journals User interface approaches implemented with automated patient deterioration surveillance tools: protocol for a scoping review

BMJ Open ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. e055525
Author(s):  
Yik-Ki Jacob Wan ◽  
Guilherme Del Fiol ◽  
Mary M McFarland ◽  
Melanie C Wright
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Adverse Events ◽  
Scoping Review ◽  
English Language ◽  
Learning Algorithms ◽  
Sudden Cardiac Arrest ◽  
Machine Learning Algorithms ◽  
Cochrane Library ◽  
Patient Deterioration

IntroductionEarly identification of patients who may suffer from unexpected adverse events (eg, sepsis, sudden cardiac arrest) gives bedside staff valuable lead time to care for these patients appropriately. Consequently, many machine learning algorithms have been developed to predict adverse events. However, little research focuses on how these systems are implemented and how system design impacts clinicians’ decisions or patient outcomes. This protocol outlines the steps to review the designs of these tools.Methods and analysisWe will use scoping review methods to explore how tools that leverage machine learning algorithms in predicting adverse events are designed to integrate into clinical practice. We will explore the types of user interfaces deployed, what information is displayed, and how clinical workflows are supported. Electronic sources include Medline, Embase, CINAHL Complete, Cochrane Library (including CENTRAL), and IEEE Xplore from 1 January 2009 to present. We will only review primary research articles that report findings from the implementation of patient deterioration surveillance tools for hospital clinicians. The articles must also include a description of the tool’s user interface. Since our primary focus is on how the user interacts with automated tools driven by machine learning algorithms, electronic tools that do not extract data from clinical data documentation or recording systems such as an EHR or patient monitor, or otherwise require manual entry, will be excluded. Similarly, tools that do not synthesise information from more than one data variable will also be excluded. This review will be limited to English-language articles. Two reviewers will review the articles and extract the data. Findings from both researchers will be compared with minimise bias. The results will be quantified, synthesised and presented using appropriate formats.Ethics and disseminationEthics review is not required for this scoping review. Findings will be disseminated through peer-reviewed publications.

scholarly journals Machine Learning Algorithms Using Logistic Regression for Predicting Neurosurgical Outcomes

2020 ◽  
Author(s):  
Nida Fatima
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Predictive Model ◽  
Web Application ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Brier Score ◽  
Patient Counselling ◽  
Selection Of Variables

Abstract Background: Preoperative prognostication of clinical and surgical outcome in patients with neurosurgical diseases can improve the risk stratification, thus can guide in implementing targeted treatment to minimize these events. Therefore, the author aims to highlight the development and validation of predictive models determining neurosurgical outcomes through machine learning algorithms using logistic regression.Methods: Logistic regression (enter, backward and forward) and least absolute shrinkage and selection operator (LASSO) method for selection of variables from selected database can eventually lead to multiple candidate models. The final model with a set of predictive variables must be selected based upon the clinical knowledge and numerical results.Results: The predictive model which performed best on the discrimination, calibration, Brier score and decision curve analysis must be selected to develop machine learning algorithms. Logistic regression should be compared with the LASSO model. Usually for the big databases, the predictive model selected through logistic regression gives higher Area Under the Curve (AUC) than those with LASSO model. The predictive probability derived from the best model could be uploaded to an open access web application which is easily deployed by the patients and surgeons to make a risk assessment world-wide.Conclusions: Machine learning algorithms provide promising results for the prediction of outcomes following cranial and spinal surgery. These algorithms can provide useful factors for patient-counselling, assessing peri-operative risk factors, and predicting post-operative outcomes after neurosurgery.

scholarly journals Air Quality Prediction by Classification of Supervised Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 516-521
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
User Interface ◽  
Air Quality ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Model Parameters ◽  
Quality Prediction ◽  
Air Quality Prediction

Generally, air pollution refer to the release of various pollutants into the air which are threatening the human health and planet as well. The air pollution is the major dangerous vicious to the humanity ever faced. It causes major damage to animals, plants etc., if this keeps on continuing, the human being will face serious situations in the upcoming years. The major pollutants are from the transport and industries. So, to prevent this problem major sectors have to predict the air quality from transport and industries .In existing project there are many disadvantages. The project is about estimating the PM2.5 concentration by designing a photograph based method. But photographic method is not alone sufficient to calculate PM2.5 because it contains only one of the concentration of pollutants and it calculates only PM2.5 so there are some missing out of the major pollutants and the information needed for controlling the pollution .So thereby we proposed the machine learning techniques by user interface of GUI application. In this multiple dataset can be combined from the different source to form a generalized dataset and various machine learning algorithms are used to get the results with maximum accuracy. From comparing various machine learning algorithms we can obtain the best accuracy result. Our evaluation gives the comprehensive manual to sensitivity evaluation of model parameters with regard to overall performance in prediction of air high quality pollutants through accuracy calculation. Additionally to discuss and compare the performance of machine learning algorithms from the dataset with evaluation of GUI based user interface air quality prediction by attributes.

scholarly journals Survival prediction for bladder cancer using machine learning: development of BlaCaSurv online survival prediction application

2020 ◽  
Author(s):  
Ashis Kumar Das ◽  
Shiba Mishra ◽  
Devi Kalyan Mishra ◽  
Saji Saraswathy Gopalan
Keyword(s):  
Machine Learning ◽  
Bladder Cancer ◽  
Cancer Patients ◽  
Web Application ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Survival Prediction ◽  
Common Cause

AbstractBackgroundBladder cancer is the most common cancer of the urinary system among the American population and it is the fourth most common cause of cancer morbidity and the eight most common cause of cancer mortality among men. Using machine learning algorithms, we predict the five-year survival among bladder cancer patients and deploy the best performing algorithm as a web application for survival prediction.MethodsMicroscopically confirmed adult bladder cancer patients were included from the Surveillance Epidemiology and End Results (SEER) database (2000-2017) and randomly split into training and test datasets (70/30 ratio). Five machine learning algorithms (logistic regression, support vector machine, gradient boosting, random forest, and K nearest neighbor) were trained on features to predict five-year survival. The algorithms were compared with performance metrics and the best performing algorithm was deployed as a web application.ResultsA total of 52,529 patients were included in our study. The gradient boosting algorithm was the best performer in terms of predictive ability and discrimination. It was deployed as the survival prediction web application named BlaCaSurv (https://blacasurv.herokuapp.com/).ConclusionsWe tested several machine learning algorithms and developed a web application for predicting five-year survival for bladder cancer patients. This application can be used as a supplementary prognostic tool to clinical decision making.

scholarly journals Embedded Machine Learning and Embedded Systems in the Industry.

2021 ◽  
Vol 9 (11) ◽  
pp. 1872-1875
Author(s):  
Pratic Chakraborty
Keyword(s):  
Machine Learning ◽  
Embedded Systems ◽  
Power Consumption ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abstract Machine ◽  
Native Languages ◽  
Processing Power ◽  
Server Computer

Abstract: Machine learning is the buzz word right now. With the machine learning algorithms one can make a computer differentiate between a human and a cow. Can detect objects, can predict different parameters and can process our native languages. But all these algorithms require a fair amount of processing power in order to be trained and fitted as a model. Thankfully, with the current improvement in technology, processing power of computers have significantly increased. But there is a limitation in power consumption and deployability of a server computer. This is where “tinyML” helps the industry out. Machine Learning has never been so easy to access before!

scholarly journals Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders

2018 ◽  
Vol 45 (5) ◽  
pp. E6 ◽  
Author(s):  
Aditya V. Karhade ◽  
Paul Ogink ◽  
Quirina Thio ◽  
Marike Broekman ◽  
Thomas Cha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Access ◽  
Spine Surgery ◽  
Web Application ◽  
Lumbar Disc ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Degenerative Disc ◽  
Preoperative Prediction ◽  
American Society

OBJECTIVEIf not anticipated and prearranged, hospital stay can be prolonged while the patient awaits placement in a rehabilitation unit or skilled nursing facility following elective spine surgery. Preoperative prediction of the likelihood of postoperative discharge to any setting other than home (i.e., nonroutine discharge) after elective inpatient spine surgery would be helpful in terms of decreasing hospital length of stay. The purpose of this study was to use machine learning algorithms to develop an open-access web application for preoperative prediction of nonroutine discharges in surgery for elective inpatient lumbar degenerative disc disorders.METHODSThe American College of Surgeons National Surgical Quality Improvement Program was queried to identify patients who underwent elective inpatient spine surgery for lumbar disc herniation or lumbar disc degeneration between 2011 and 2016. Four machine learning algorithms were developed to predict nonroutine discharge and the best algorithm was incorporated into an open-access web application.RESULTSThe rate of nonroutine discharge for 26,364 patients who underwent elective inpatient surgery for lumbar degenerative disc disorders was 9.28%. Predictive factors selected by random forest algorithms were age, sex, body mass index, fusion, level, functional status, extent and severity of comorbid disease (American Society of Anesthesiologists classification), diabetes, and preoperative hematocrit level. On evaluation in the testing set (n = 5273), the neural network had a c-statistic of 0.823, calibration slope of 0.935, calibration intercept of 0.026, and Brier score of 0.0713. On decision curve analysis, the algorithm showed greater net benefit for changing management over all threshold probabilities than changing management on the basis of the American Society of Anesthesiologists classification alone or for all patients or for no patients. The model can be found here: https://sorg-apps.shinyapps.io/discdisposition/.CONCLUSIONSMachine learning algorithms show promising results on internal validation for preoperative prediction of nonroutine discharges. If found to be externally valid, widespread use of these algorithms via the open-access web application by healthcare professionals may help preoperative risk stratification of patients undergoing elective surgery for lumbar degenerative disc disorders.

scholarly journals Applying Unsupervised Machine Learning in Continuous Integration, Security and Deployment Pipeline Automation for Application Software System

2019 ◽  
Vol 8 (4) ◽  
pp. 1426-1430
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Application Software ◽  
Unsupervised Machine Learning ◽  
Continuous Integration ◽  
And Performance ◽  
Agile Software

Continuous integration and Continuous Deployment (CICD) is a trending practice in agile software development. Using Continuous Integration helps the developers to find the bugs before it goes to the production by running unit tests, smoke tests etc. Deploying the components of the application in Production using Continuous Deployment, using this way, the new release of the application reaches the client faster. Continuous Security makes sure that the application is less prone to vulnerabilities by doing static scans on code and dynamic scans on the deployed releases. The goal of this study is to identify the benefits of adapting the Continuous Integration - Continuous Deployment in Application Software. The Pipeline involves Implementation of CI – CS - CD on a web application ClubSoc which is a Club Management Application and using unsupervised machine learning algorithms to detect anomalies in the CI-CS-CD process. The Continuous Integration is implemented using Jenkins CI Tool, Continuous Security is implemented using Veracode Tool and Continuous Deployment is done using Docker and Jenkins. The results have shown by adapting this methodology, the author is able to improve the quality of the code, finding vulnerabilities using static scans, portability and saving time by automation in deployment of applications using Docker and Jenkins. Applying machine learning helps in predicting the defects, failures and trends in the Continuous Integration Pipeline, whereas it can help in predicting the business impact in Continuous Delivery. Unsupervised learning algorithms such as K-means Clustering, Symbolic Aggregate Approximation (SAX) and Markov are used for Quality and Performance Regression analysis in the CICD Model. Using the CICD model, the developers can fix the bugs pre-release and this will impact the company as a whole by raising the profit and attracting more customers. The Updated Application reaches the client faster using Continuous Deployment. By Analyzing the failure trends using Unsupervised machine learning, the developers might be able to predict where the next error is likely to happen and prevent it in the pre-build stage

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