scholarly journals Keratoconus Diagnostic and Treatment Algorithms Based on Machine-Learning Methods

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1933
Author(s):  
Boris Malyugin ◽  
Sergej Sakhnov ◽  
Svetlana Izmailova ◽  
Ernest Boiko ◽  
Nadezhda Pozdeyeva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Treatment Strategies ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Clinical Environment ◽  
Visual Rehabilitation ◽  
Web Based ◽  
Treatment Algorithms ◽  
Management Algorithm ◽  
Learning Techniques

The accurate diagnosis of keratoconus, especially in its early stages of development, allows one to utilise timely and proper treatment strategies for slowing the progression of the disease and provide visual rehabilitation. Various keratometry indices and classifications for quantifying the severity of keratoconus have been developed. Today, many of them involve the use of the latest methods of computer processing and data analysis. The main purpose of this work was to develop a machine-learning-based algorithm to precisely determine the stage of keratoconus, allowing optimal management of patients with this disease. A multicentre retrospective study was carried out to obtain a database of patients with keratoconus and to use machine-learning techniques such as principal component analysis and clustering. The created program allows for us to distinguish between a normal state; preclinical keratoconus; and stages 1, 2, 3 and 4 of the disease, with an accuracy in terms of the AUC of 0.95 to 1.00 based on keratotopographer readings, relative to the adapted Amsler–Krumeich algorithm. The predicted stage and additional diagnostic criteria were then used to create a standardised keratoconus management algorithm. We also developed a web-based interface for the algorithm, providing us the opportunity to use the software in a clinical environment.

scholarly journals A Study of University Website Content Classification Using Machine Learning

2021 ◽  
Vol 2129 (1) ◽  
pp. 012043
Author(s):  
H R Mohd Sharul ◽  
I Nor Azman ◽  
M Mohd Su Elya
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Web Pages ◽  
Web Content ◽  
Web Based ◽  
Content Classification ◽  
Learning Techniques ◽  
Information Products ◽  
Website Content ◽  
New Framework

Abstract A university website is a gateway to the institution’s information, products, and services. As websites grow into millions in numbers, it is essential to ensure that the content reflects the needs of its students, staff, and other academic institution as their primary users. This research investigates the development of a new framework that uses machine learning techniques based on webometrics and web usability to classify the web pages of academic websites automatically. The framework briefly introduced how it can help classify web content and eliminate unrelated content and reduce storage space. The findings can also be used to analyse other web-based data to give additional insights that may be beneficial for webometrics studies and identify university website’ characteristics.

Identification of Losses in Turbomachinery With Machine Learning

2020 ◽  
Author(s):  
Gino Angelini ◽  
Alessandro Corsini ◽  
Giovanni Delibra ◽  
Marco Giovannelli
Keyword(s):  
Machine Learning ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Post Processing ◽  
Pressure Losses ◽  
Learning Techniques ◽  
Flow Features ◽  
Exploratory Data ◽  
Unsupervised Approach ◽  
Important Design

Abstract One of the issues of handling large CFD datasets and process them to derive important design correlations is the limitation in automating the post-processing of data. Machine learning techniques, developed to process large unlabelled dataset, can play a key role on this subject. In this work an unsupervised approach to isolate different flow features inside a 2D cascade is proposed and validated. The approach relies on machine learning methods and in particular on Exploratory Data Analysis (EDA) and Principal Component Analysis for the pre-processing of the data and on K-means clustering for the post-processing. The K-means algorithm was trained on a Design of Experiments (DoE) of over 140 cases of 2D linear cascade configurations to identify the boundary layer on the profiles and the wake downstream. Validation resulted in a perfect capability of identifying the regions of interest. Then a possible exploitation of this method is presented, to compute pressure losses downstream of the cascade and train an artificial neural network to make a regression able to extend data to all the possible combinations of geometrical and operating parameters of the cascade. The same algorithm was applied to 3D flow cascades of profiles with sinusoidal leading edges to stress its extrapolation capability in case of flow regimes not present in the training DoE.

A Pharmaco-Cybernetics Approach to Patient Safety

2016 ◽  
pp. 1445-1464
Author(s):  
Kevin Yi-Lwern Yap
Keyword(s):  
Machine Learning ◽  
Patient Safety ◽  
Cancer Patients ◽  
Adverse Drug Reactions ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Drug Reactions ◽  
Internet Applications ◽  
Case Examples ◽  
Learning Techniques

Pharmaco-cybernetics is an upcoming interdisciplinary field that supports our use of medicines and drugs through the combined use of computational technologies and techniques with human-computer-environment interactions to reduce or prevent drug-related problems. The advent of pharmaco-cybernetics has led to the development of various software, tools, and Internet applications that can be used by healthcare practitioners to deliver optimum pharmaceutical care and health-related outcomes. Patients are becoming more informed through health information on the Internet, which empowers them to better participate in the management of their own conditions. Focusing on patients with cancer, this chapter describes the use of a pharmaco-cybernetics approach to identify clinically relevant predictors of two debilitating adverse drug reactions, which are a cause of patient safety – chemotherapy-induced nausea and vomiting and febrile neutropenia. The early identification of such clinical predictors enables clinicians to prevent or reduce the occurrence of adverse drug reactions in cancer patients undergoing chemotherapy through appropriate management strategies. The computational methods used in this approach involve two unsupervised machine-learning techniques – principal component and multiple correspondence analyses. Using two case examples, this chapter shows the potential of machine-learning techniques for identifying patients who are at greater risks of these adverse drug reactions, thus enhancing patient safety. This chapter also aims to increase the awareness among healthcare professionals and clinician-scientists about the usefulness of such techniques in clinical patient populations, so that these can be considered as part of clinical care pathways to enhance patient safety and effectively manage cancer patients on chemotherapy.

A Pharmaco-Cybernetics Approach to Patient Safety

2014 ◽  
pp. 179-197
Author(s):  
Kevin Yi-Lwern Yap
Keyword(s):  
Machine Learning ◽  
Patient Safety ◽  
Cancer Patients ◽  
Adverse Drug Reactions ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Drug Reactions ◽  
Internet Applications ◽  
Case Examples ◽  
Learning Techniques

Pharmaco-cybernetics is an upcoming interdisciplinary field that supports our use of medicines and drugs through the combined use of computational technologies and techniques with human-computer-environment interactions to reduce or prevent drug-related problems. The advent of pharmaco-cybernetics has led to the development of various software, tools, and Internet applications that can be used by healthcare practitioners to deliver optimum pharmaceutical care and health-related outcomes. Patients are becoming more informed through health information on the Internet, which empowers them to better participate in the management of their own conditions. Focusing on patients with cancer, this chapter describes the use of a pharmaco-cybernetics approach to identify clinically relevant predictors of two debilitating adverse drug reactions, which are a cause of patient safety – chemotherapy-induced nausea and vomiting and febrile neutropenia. The early identification of such clinical predictors enables clinicians to prevent or reduce the occurrence of adverse drug reactions in cancer patients undergoing chemotherapy through appropriate management strategies. The computational methods used in this approach involve two unsupervised machine-learning techniques – principal component and multiple correspondence analyses. Using two case examples, this chapter shows the potential of machine-learning techniques for identifying patients who are at greater risks of these adverse drug reactions, thus enhancing patient safety. This chapter also aims to increase the awareness among healthcare professionals and clinician-scientists about the usefulness of such techniques in clinical patient populations, so that these can be considered as part of clinical care pathways to enhance patient safety and effectively manage cancer patients on chemotherapy.

Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques

2022 ◽  
pp. 209-232
Author(s):  
Xiang Li ◽  
Jingxi Liao ◽  
Tianchuan Gao
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Data Science ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Classification Models ◽  
Performance Measurements ◽  
Learning Techniques ◽  
Source Data ◽  
Bed And Breakfast

Machine learning is a broad field that contains multiple fields of discipline including mathematics, computer science, and data science. Some of the concepts, like deep neural networks, can be complicated and difficult to explain in several words. This chapter focuses on essential methods like classification from supervised learning, clustering, and dimensionality reduction that can be easily interpreted and explained in an acceptable way for beginners. In this chapter, data for Airbnb (Air Bed and Breakfast) listings in London are used as the source data to study the effect of each machine learning technique. By using the K-means clustering, principal component analysis (PCA), random forest, and other methods to help build classification models from the features, it is able to predict the classification results and provide some performance measurements to test the model.

scholarly journals Development of accurate classification of heavenly bodies using novel machine learning techniques

2021 ◽  
Vol 25 (10) ◽  
pp. 7213-7228
Author(s):  
Michał Wierzbiński ◽  
Paweł Pławiak ◽  
Mohamed Hammad ◽  
U. Rajendra Acharya
Keyword(s):  
Machine Learning ◽  
Outer Space ◽  
Principal Component ◽  
Sloan Digital Sky Survey ◽  
Machine Learning Techniques ◽  
Evolutionary System ◽  
Stellar Spectra ◽  
Learning Techniques ◽  
Machine Learning Model

AbstractThe heavenly bodies are objects that swim in the outer space. The classification of these objects is a challenging task for astronomers. This article presents a novel methodology that enables an efficient and accurate classification of cosmic objects (3 classes) based on evolutionary optimization of classifiers. This research collected the data from Sloan Digital Sky Survey database. In this work, we are proposing to develop a novel machine learning model to classify stellar spectra of stars, quasars and galaxies. First, the input data are normalized and then subjected to principal component analysis to reduce the dimensionality. Then, the genetic algorithm is implemented on the data which helps to find the optimal parameters for the classifiers. We have used 21 classifiers to develop an accurate and robust classification with fivefold cross-validation strategy. Our developed model has achieved an improvement in the accuracy using nineteen out of twenty-one models. We have obtained the highest classification accuracy of 99.16%, precision of 98.78%, recall of 98.08% and F1-score of 98.32% using evolutionary system based on voting classifier. The developed machine learning prototype can help the astronomers to make accurate classification of heavenly bodies in the sky. Proposed evolutionary system can be used in other areas where accurate classification of many classes is required.

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