scholarly journals Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach (Preprint)

2020 ◽  
Author(s):  
Cheng-Sheng Yu ◽  
Yu-Jiun Lin ◽  
Chang-Hsien Lin ◽  
Shiyng-Yu Lin ◽  
Jenny L Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Preventive Medicine ◽  
Roc Curve ◽  
Database System ◽  
Learning System ◽  
The Internet ◽  
Health Examination ◽  
Medical Database ◽  
Care Assessment

BACKGROUND In the era of information explosion, the use of the internet to assist with clinical practice and diagnosis has become a cutting-edge area of research. The application of medical informatics allows patients to be aware of their clinical conditions, which may contribute toward the prevention of several chronic diseases and disorders. OBJECTIVE In this study, we applied machine learning techniques to construct a medical database system from electronic medical records (EMRs) of subjects who have undergone health examination. This system aims to provide online self-health evaluation to clinicians and patients worldwide, enabling personalized health and preventive health. METHODS We built a medical database system based on the literature, and data preprocessing and cleaning were performed for the database. We utilized both supervised and unsupervised machine learning technology to analyze the EMR data to establish prediction models. The models with EMR databases were then applied to the internet platform. RESULTS The validation data were used to validate the online diagnosis prediction system. The accuracy of the prediction model for metabolic syndrome reached 91%, and the area under the receiver operating characteristic (ROC) curve was 0.904 in this system. For chronic kidney disease, the prediction accuracy of the model reached 94.7%, and the area under the ROC curve (AUC) was 0.982. In addition, the system also provided disease diagnosis visualization via clustering, allowing users to check their outcome compared with those in the medical database, enabling increased awareness for a healthier lifestyle. CONCLUSIONS Our web-based health care machine learning system allowed users to access online diagnosis predictions and provided a health examination report. Users could understand and review their health status accordingly. In the future, we aim to connect hospitals worldwide with our platform, so that health care practitioners can make diagnoses or provide patient education to remote patients. This platform can increase the value of preventive medicine and telemedicine.

scholarly journals Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach

10.2196/18585 ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. e18585 ◽  
Author(s):  
Cheng-Sheng Yu ◽  
Yu-Jiun Lin ◽  
Chang-Hsien Lin ◽  
Shiyng-Yu Lin ◽  
Jenny L Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Preventive Medicine ◽  
Roc Curve ◽  
Database System ◽  
Learning System ◽  
The Internet ◽  
Health Examination ◽  
Medical Database ◽  
Care Assessment

Background In the era of information explosion, the use of the internet to assist with clinical practice and diagnosis has become a cutting-edge area of research. The application of medical informatics allows patients to be aware of their clinical conditions, which may contribute toward the prevention of several chronic diseases and disorders. Objective In this study, we applied machine learning techniques to construct a medical database system from electronic medical records (EMRs) of subjects who have undergone health examination. This system aims to provide online self-health evaluation to clinicians and patients worldwide, enabling personalized health and preventive health. Methods We built a medical database system based on the literature, and data preprocessing and cleaning were performed for the database. We utilized both supervised and unsupervised machine learning technology to analyze the EMR data to establish prediction models. The models with EMR databases were then applied to the internet platform. Results The validation data were used to validate the online diagnosis prediction system. The accuracy of the prediction model for metabolic syndrome reached 91%, and the area under the receiver operating characteristic (ROC) curve was 0.904 in this system. For chronic kidney disease, the prediction accuracy of the model reached 94.7%, and the area under the ROC curve (AUC) was 0.982. In addition, the system also provided disease diagnosis visualization via clustering, allowing users to check their outcome compared with those in the medical database, enabling increased awareness for a healthier lifestyle. Conclusions Our web-based health care machine learning system allowed users to access online diagnosis predictions and provided a health examination report. Users could understand and review their health status accordingly. In the future, we aim to connect hospitals worldwide with our platform, so that health care practitioners can make diagnoses or provide patient education to remote patients. This platform can increase the value of preventive medicine and telemedicine.

scholarly journals Correction: Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach

10.2196/21753 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e21753
Author(s):  
Cheng-Sheng Yu ◽  
Yu-Jiun Lin ◽  
Chang-Hsien Lin ◽  
Shiyng-Yu Lin ◽  
Jenny L Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Preventive Medicine ◽  
Learning Approach ◽  
Care Assessment ◽  
Machine Learning Approach

ROBOTIC ARM WITH BIoT MACHINE LEARNING SYSTEM

Fractals ◽  
2020 ◽  
Vol 28 (04) ◽  
pp. 2050088
Author(s):  
R. CARREÑO AGUILERA ◽  
F. AGUILAR ACEVEDO ◽  
M. PATIÑO ORTIZ ◽  
J. PATIÑO ORTIZ
Keyword(s):  
Machine Learning ◽  
Expert System ◽  
Internet Of Things ◽  
Reference Points ◽  
Learning System ◽  
Artificial Vision ◽  
Robotic Arm ◽  
The Internet ◽  
Limiting Factor ◽  
Flat Surfaces

In this work, we present a robotic arm assisted by a visual system to decide whether an object with different colors, parallel flat surfaces and other types of surfaces would be subject to be manipulated without a drop risk. This robotic arm is assisted with sensors such as temperature, humidity, artificial vision, etc. and monitored with a Blockchain Internet of Things (BIoT) expert system assistance, which is shared and accessed by the internet by the users. A prototype for industrial purpose is launched to start providing data for training the expert system, achieving in this way an expert system with machine learning. The variations derived from the identification of the reference points and the characteristics of the robotic arm are a limiting factor of the system, however, it was possible to correctly locate the robotic arm in the workspace to take the object and manipulate it using machine learning based on a BIoT expert system.

P5710Clinical applications of machine learning for prediction of incident atrial fibrillation from the general population: a nationwide cohort study

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
I.-S Kim ◽  
P S Yang ◽  
H T Yu ◽  
T H Kim ◽  
J S Uhm ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Logistic Regression ◽  
General Population ◽  
Regression Model ◽  
National Health ◽  
Logistic Regression Model ◽  
Learning System ◽  
Health Examination ◽  
Clinical Variables

Abstract Background To evaluate the ability of machine learning algorithms to predict incident atrial fibrillation (AF) from the general population using health examination items. Methods We included 483,343 subjects who received national health examinations from the Korean National Health Insurance Service-based National Sample Cohort (NHIS-NSC). We trained deep neural network model (DNN) of a deep learning system and decision tree model (DT) of a machine learning system using clinical variables and health examination items (including age, sex, body mass index, history of heart failure, hypertension or diabetes, baseline creatinine, and smoking and alcohol intake habits) to predict incident AF using a training dataset of 341,771 subjects constructed from the NHIS-NSC database. The DNN and DT were validated using an independent test dataset of 141,572 remaining subjects. C-indices of DNN and DT for prediction of incident AF were compared with that of conventional logistic regression model. Results During 1,874,789 person·years (mean±standard-deviation age 47.7±14.4 years, 49.6% male), 3,282 subjects with incident AF were observed. In the validation dataset, 1,139 subjects with incident AF were observed. The c-indices of the DNN and DT for incident AF prediction were 0.828 [0.819–0.836] and 0.835 [0.825–0.844], and were significantly higher (p<0.01) than conventional logistic regression model (c-index=0.789 [0.784–0.794]). Conclusions Application of machine learning using simple clinical variables and health examination items was helpful to predict incident AF in the general population. Prospective study is warranted to construct an individualized precision medicine.

scholarly journals A Blockchain-Based Intelligent Machine Learning System for Smart Health Care

2021 ◽  
Author(s):  
Mir Hassan ◽  
Remigijus Paulavicius ◽  
Ernestas Filatovas ◽  
Adnan Iftekhar
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Care System ◽  
Patient Autonomy ◽  
Learning System ◽  
Security Level ◽  
Blockchain Technology ◽  
Smart Health ◽  
Intelligent Methods ◽  
Care System

Blockchain and Machine Learning gives the best solutions together in performing various tasks in the Smart Health care system. With these two new emerging technologies, that have materialized in the last decade. In this paper, we proposed secure, transparent and intelligent methods in the Internate of medical things industry using Machine learning models and blockchain technology to enhance security level and train our models to improve diagnostic, prevention, treatment of the patient, patient rights, patient autonomy and equality in the health care system.

scholarly journals Towards Intelligent E-Learning Systems: A Hybrid Model for Predicatingthe Learning Continuity in Iraqi Higher Education

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 172-188
Author(s):  
Mohammed K. Kadhim ◽  
Alia K. Hassan
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Neural Networks ◽  
Hybrid Model ◽  
Educational Process ◽  
Learning Systems ◽  
Learning System ◽  
The Internet ◽  
Learning Methods ◽  
E Learning

E-Learning system gains a great attention in the past years; with advance of the internet and the information exchange techniques the importance to merge the traditional learning means with the internet-based learning methods became a must especially in Iraq, the Iraqi higher education is now coping with the new information and communication technologies and adopting a modern methods for upgrading their education and learning ways. There are great efforts to blend E-Learning systems with the educational process, in order to fulfill this purposes the proposed research is advancing E-Learning systems by suggesting a hybrid method that combines two Artificial Intelligence Techniques (AI) inside the design and the development of an intelligent E-Learning system for computer science department at university of technology. The utilization of Artificial Neural Networks algorithm (ANNs) especially Recurrent Neural Networks (RNN) is a way of implementing deep learning technique to predict the students' final out comes in virtual class room based on their grades and their learning behaviors. RNN is optimized by utilizing ADAM optimizer to lift the accuracy of the proposed algorithm, the dataset are gathered and processed to suite the education purposes and was divided into80% for training the model and 20% for testing the model, the results of the hybrid model are compared with other machine learning methods like Multi-Layer Perceptron (MLP), decision tree, naïve Bayesian, and random forest using WEKA environment, the results of the proposed model showed a promising accuracy when compared with the mentioned machine learning algorithms.

PEMBELAJARAN E-LEARNING

2019 ◽  
Vol 2 ◽  
Author(s):  
Nuke Lulu Ul Chusna
Keyword(s):  
Subject Matter ◽  
Technological Progress ◽  
Learning Culture ◽  
Learning System ◽  
Internet Technology ◽  
The Internet ◽  
Digital Format ◽  
E Learning ◽  
Context Of Learning ◽  
Present Subject

The internet can be used as a way to transfer knowledge from teachers to students. Learning that utilizes the development of technology and information, namely the internet, one of which is the e-learning learning system. E-learning is a form of conventional learning that is transferred in digital format through internet technology, not only to present subject matter on the internet but also must be in accordance with the principles of learning.The e-learning learning model results in changes in learning culture in the context of learning. Learning becomes very flexible, because it can be adjusted to the availability of time from students in learning the material provided by the teacher.The teacher determines the success of students in learning, therefore teachers are required to have the ability to adapt to technological progress. Keywords: ICT,e-learning, e-learning learning

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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