scholarly journals Detecting COVID-19 in chest images based on deep transfer learning and machine learning algorithms

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Seyed Masoud Rezaeijo ◽  
Mohammadreza Ghorvei ◽  
Razzagh Abedi-Firouzjah ◽  
Hesam Mojtahedi ◽  
Hossein Entezari Zarch
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Learning Algorithms ◽  
Chest Ct ◽  
Machine Learning Algorithms ◽  
Limited Resources ◽  
Learning Models ◽  
Laboratory Equipment ◽  
Middle Income ◽  
Middle Income Countries

Abstract Background This study aimed to propose an automatic prediction of COVID-19 disease using chest CT images based on deep transfer learning models and machine learning (ML) algorithms. Results The dataset consisted of 5480 samples in two classes, including 2740 CT chest images of patients with confirmed COVID-19 and 2740 images of suspected cases was assessed. The DenseNet201 model has obtained the highest training with an accuracy of 100%. In combining pre-trained models with ML algorithms, the DenseNet201 model and KNN algorithm have received the best performance with an accuracy of 100%. Created map by t-SNE in the DenseNet201 model showed not any points clustered with the wrong class. Conclusions The mentioned models can be used in remote places, in low- and middle-income countries, and laboratory equipment with limited resources to overcome a shortage of radiologists.

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

2021 ◽  
Vol 9 (VI) ◽  
pp. 3605-3611
Author(s):  
Pratyush Kaware
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Low Cost ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

scholarly journals Execution Assessment of Machine Learning Algorithms for Spam Profile Detection on Instagram

2021 ◽  
Vol 10 (3) ◽  
pp. 1889-1894
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Tools ◽  
Learning Models ◽  
K Nearest Neighbor

Witheverypassingsecondsocialnetworkcommunityisgrowingrapidly,becauseofthat,attackershaveshownkeeninterestinthesekindsofplatformsandwanttodistributemischievouscontentsontheseplatforms.Withthefocus on introducing new set of characteristics and features forcounteractivemeasures,agreatdealofstudieshasresearchedthe possibility of lessening the malicious activities on social medianetworks. This research was to highlight features for identifyingspammers on Instagram and additional features were presentedto improve the performance of different machine learning algorithms. Performance of different machine learning algorithmsnamely, Multilayer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)were evaluated on machine learning tools named, RapidMinerand WEKA. The results from this research tells us that RandomForest (RF) outperformed all other selected machine learningalgorithmsonbothselectedmachinelearningtools.OverallRandom Forest (RF) provided best results on RapidMiner. Theseresultsareusefulfortheresearcherswhoarekeentobuildmachine learning models to find out the spamming activities onsocialnetworkcommunities.

scholarly journals Imputation of PaO2 from SpO2 values from the MIMIC-III Critical Care Database Using Machine-Learning Based Algorithms

2021 ◽  
Author(s):  
Shuangxia Ren ◽  
Jill Zupetic ◽  
Mehdi Nouraie ◽  
Xinghua Lu ◽  
Richard D. Boyce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Learning Models ◽  
Mechanically Ventilated ◽  
Mechanically Ventilated Patients ◽  
Mimic Iii ◽  
Ventilated Patients ◽  
Machine Learning Models

AbstractBackgroundThe partial pressure of oxygen (PaO2)/fraction of oxygen delivered (FIO2) ratio is the reference standard for assessment of hypoxemia in mechanically ventilated patients. Non-invasive monitoring with the peripheral saturation of oxygen (SpO2) is increasingly utilized to estimate PaO2 because it does not require invasive sampling. Several equations have been reported to impute PaO2/FIO2 from SpO2 /FIO2. However, machine-learning algorithms to impute the PaO2 from the SpO2 has not been compared to published equations.Research QuestionHow do machine learning algorithms perform at predicting the PaO2 from SpO2 compared to previously published equations?MethodsThree machine learning algorithms (neural network, regression, and kernel-based methods) were developed using 7 clinical variable features (n=9,900 ICU events) and subsequently 3 features (n=20,198 ICU events) as input into the models from data available in mechanically ventilated patients from the Medical Information Mart for Intensive Care (MIMIC) III database. As a regression task, the machine learning models were used to impute PaO2 values. As a classification task, the models were used to predict patients with moderate-to-severe hypoxemic respiratory failure based on a clinically relevant cut-off of PaO2/FIO2 ≤ 150. The accuracy of the machine learning models was compared to published log-linear and non-linear equations. An online imputation calculator was created.ResultsCompared to seven features, three features (SpO2, FiO2 and PEEP) were sufficient to impute PaO2/FIO2 ratio using a large dataset. Any of the tested machine learning models enabled imputation of PaO2/FIO2 from the SpO2/FIO2 with lower error and had greater accuracy in predicting PaO2/FIO2 ≤ 150 compared to published equations. Using three features, the machine learning models showed superior performance in imputing PaO2 across the entire span of SpO2 values, including those ≥ 97%.InterpretationThe improved performance shown for the machine learning algorithms suggests a promising framework for future use in large datasets.

scholarly journals Using Big Data-machine learning models for diabetes prediction and flight delays analytics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Thérence Nibareke ◽  
Jalal Laassiri
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Linear Regression ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Learning Models ◽  
Flight Delays ◽  
Machine Learning Models

Abstract Introduction Nowadays large data volumes are daily generated at a high rate. Data from health system, social network, financial, government, marketing, bank transactions as well as the censors and smart devices are increasing. The tools and models have to be optimized. In this paper we applied and compared Machine Learning algorithms (Linear Regression, Naïve bayes, Decision Tree) to predict diabetes. Further more, we performed analytics on flight delays. The main contribution of this paper is to give an overview of Big Data tools and machine learning models. We highlight some metrics that allow us to choose a more accurate model. We predict diabetes disease using three machine learning models and then compared their performance. Further more we analyzed flight delay and produced a dashboard which can help managers of flight companies to have a 360° view of their flights and take strategic decisions. Case description We applied three Machine Learning algorithms for predicting diabetes and we compared the performance to see what model give the best results. We performed analytics on flights datasets to help decision making and predict flight delays. Discussion and evaluation The experiment shows that the Linear Regression, Naive Bayesian and Decision Tree give the same accuracy (0.766) but Decision Tree outperforms the two other models with the greatest score (1) and the smallest error (0). For the flight delays analytics, the model could show for example the airport that recorded the most flight delays. Conclusions Several tools and machine learning models to deal with big data analytics have been discussed in this paper. We concluded that for the same datasets, we have to carefully choose the model to use in prediction. In our future works, we will test different models in other fields (climate, banking, insurance.).

scholarly journals SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach

2018 ◽  
Vol 10 (8) ◽  
pp. 76 ◽  
Author(s):  
Marcio Teixeira ◽  
Tara Salman ◽  
Maede Zolanvari ◽  
Raj Jain ◽  
Nader Meskin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supervisory Control ◽  
Network Traffic ◽  
Learning Algorithms ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Scada System ◽  
Machine Learning Approach ◽  
Machine Learning Models

This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank’s control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naïve Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments.

scholarly journals Detection of Unilateral Arm Paresis after Stroke by Wearable Accelerometers and Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7784
Author(s):  
Johan Wasselius ◽  
Eric Lyckegård Finn ◽  
Emma Persson ◽  
Petter Ericson ◽  
Christina Brogårdh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Accelerometer Data ◽  
Learning Models ◽  
Stroke Patients ◽  
Convolutional Network ◽  
Stroke Treatment ◽  
Data Windows

Recent advances in stroke treatment have provided effective tools to successfully treat ischemic stroke, but still a majority of patients are not treated due to late arrival to hospital. With modern stroke treatment, earlier arrival would greatly improve the overall treatment results. This prospective study was performed to asses the capability of bilateral accelerometers worn in bracelets 24/7 to detect unilateral arm paralysis, a hallmark symptom of stroke, early enough to receive treatment. Classical machine learning algorithms as well as state-of-the-art deep neural networks were evaluated on detection times between 15 min and 120 min. Motion data were collected using triaxial accelerometer bracelets worn on both arms for 24 h. Eighty-four stroke patients with unilateral arm motor impairment and 101 healthy subjects participated in the study. Accelerometer data were divided into data windows of different lengths and analyzed using multiple machine learning algorithms. The results show that all algorithms performed well in separating the two groups early enough to be clinically relevant, based on wrist-worn accelerometers. The two evaluated deep learning models, fully convolutional network and InceptionTime, performed better than the classical machine learning models with an AUC score between 0.947–0.957 on 15 min data windows and up to 0.993–0.994 on 120 min data windows. Window lengths longer than 90 min only marginally improved performance. The difference in performance between the deep learning models and the classical models was statistically significant according to a non-parametric Friedman test followed by a post-hoc Nemenyi test. Introduction of wearable stroke detection devices may dramatically increase the portion of stroke patients eligible for revascularization and shorten the time to treatment. Since the treatment effect is highly time-dependent, early stroke detection may dramatically improve stroke outcomes.

scholarly journals Software Engineering for Machine Learning in Health Informatics

2019 ◽  
Author(s):  
Mohammed Moreb ◽  
Oguz Ata
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Health Informatics ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Prototype System ◽  
Machine Learning Algorithm ◽  
Learning Models ◽  
Machine Learning Models

Abstract Background We propose a novel framework for health Informatics: framework and methodology of Software Engineering for machine learning in Health Informatics (SEMLHI). This framework shed light on its features, that allow users to study and analyze the requirements, determine the function of objects related to the system and determine the machine learning algorithms that will be used for the dataset.Methods Based on original data that collected from the hospital in Palestine government in the past three years, first the data validated and all outlier removed, analyzed using develop framework in order to compare ML provide patients with real-time. Our proposed module comparison with three Systems Engineering Methods Vee, agile and SEMLHI. The result used by implement prototype system, which require machine learning algorithm, after development phase, questionnaire deliver to developer to indicate the result using three methodology. SEMLHI framework, is composed into four components: software, machine learning model, machine learning algorithms, and health informatics data, Machine learning Algorithm component used five algorithms use to evaluate the accuracy for machine learning models on component.Results we compare our approach with the previously published systems in terms of performance to evaluate the accuracy for machine learning models, the results of accuracy with different algorithms applied for 750 case, linear SVG have about 0.57 value compared with KNeighbors classifier, logistic regression, multinomial NB, random forest classifier. This research investigates the interaction between SE, and ML within the context of health informatics, our proposed framework define the methodology for developers to analyzing and developing software for the health informatic model, and create a space, in which software engineering, and ML experts could work on the ML model lifecycle, on the disease level and the subtype level.Conclusions This article is an ongoing effort towards defining and translating an existing research pipeline into four integrated modules, as framework system using the dataset from healthcare to reduce cost estimation by using a new suggested methodology. The framework is available as open source software, licensed under GNU General Public License Version 3 to encourage others to contribute to the future development of the SEMLHI framework.

scholarly journals Benchmarking of Machine Learning Models to Assist the Prognosis of Tuberculosis

2021 ◽  
Author(s):  
Maicon Herverton Lino Ferreira da Silva Barros ◽  
Geovanne Oliveira Alves ◽  
Lubnnia Morais Florêncio Souza ◽  
Élisson da Silva Rocha ◽  
João Fausto Lorenzato de Oliveira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Laboratory Data ◽  
Gradient Boosting ◽  
Learning Models ◽  
Middle Income ◽  
Middle Income Countries ◽  
Probability Of Death ◽  
Health Database ◽  
Incorrect Data ◽  
Machine Learning Models

Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. This work performs a benchmarking of machine learning models using a Brazilian health database related to TB confirmed cases and deaths, named SINAN-TB. The goal is to predict the probability of death by TB, assisting the TB prognosis and decision taking process. The database originally has 130 features, and many of these features had missing data, or incorrect data regarding the notification dates or birth dates, or were not related to the clinical and laboratory data. These data are treated, and after the preprocessing step, a new database with 38 features and 24,015 records is generated, having 22,876 TB cases and 1,139 deaths by TB. We design two experiments to investigated how the data unbalancing impacts on the models performance. With the evaluation of the f1-macro metric, we verify that the best result is achieved when using the imbalanced database, with the ensemble model that is composed of gradient boosting (GB), random forest (RF) and multi-layer perceptron (MLP) models.

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