Automated detection and forecasting of COVID-19 using deep learning techniques: A review

Neurocomputing, Journal Year: 2024, Volume and Issue: 577, P. 127317 - 127317

Published: Jan. 26, 2024

Language: Английский

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Applying Machine Learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1132 - 1132

Published: Jan. 23, 2025

Reliably detecting COVID-19 is critical for diagnosis and disease control. However, imbalanced data in medical datasets pose significant challenges machine learning models, leading to bias poor generalization. The dataset obtained from the EPIVIGILA system Chilean Epidemiological Surveillance Process contains information on over 6,000,000 patients, but, like many current datasets, it suffers class imbalance. To address this issue, we applied various algorithms, both with without sampling methods, compared them using different classification diagnostic metrics such as precision, sensitivity, specificity, likelihood ratio positive, odds ratio. Our results showed that applying methods improved metric values contributed models better Effectively managing crucial reliable diagnosis. This study enhances understanding of how techniques can improve reliability contribute patient outcomes.

Language: Английский

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The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

Oeconomia Copernicana, Journal Year: 2024, Volume and Issue: 15(1), P. 27 - 58

Published: March 30, 2024

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis symptom tracing, optimize intensive care unit admission, use clinical data to determine patient prioritization mortality risk, being pivotal qualitative provision, reducing errors, increasing survival rates, thus diminishing the massive healthcare system burden relation severe inpatient stay duration, while operational costs throughout organizational management of hospitals. Data-driven financial scenario-based contingency planning, predictive modelling tools, risk pooling mechanisms should be deployed for additional equipment unforeseen demand expenses. Purpose article: We show that deep decision making systems likelihood treatment outcomes with regard susceptible, infected, recovered individuals, performing accurate analyses by modeling based on vital signs, surveillance data, infection-related biomarkers, furthering hospital facility optimization terms bed allocation. Methods: The review software employed article screening quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, SRDR. Findings & value added: support tools forecast spread, confirmed cases, infection rates data-driven appropriate resource allocations effective therapeutic protocol development, determining suitable measures regulations using symptoms comorbidities, laboratory records across units, impacting financing infrastructure. As a result heightened personal protective equipment, pharmacy medication, outpatient treatment, supplies, revenue loss vulnerability occur, also due expenses related hiring staff critical expenditures. Hospital care, screening, capacity expansion, lead further losses affecting frontline workers patients.

Language: Английский

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Internet of Things with Deep Learning Techniques for Pandemic Detection: A Comprehensive Review of Current Trends and Open Issues

Electronics, Journal Year: 2024, Volume and Issue: 13(13), P. 2630 - 2630

Published: July 4, 2024

Technological advancements for diverse aspects of life have been made possible by the swift development and application Internet Things (IoT) based technologies. IoT technologies are primarily intended to streamline various processes, guarantee system (technology or process) efficiency, ultimately enhance quality life. An effective method pandemic detection is combination deep learning (DL) techniques with IoT. proved beneficial in many healthcare domains, especially during last worldwide health crisis: COVID-19 pandemic. Using studies published between 2019 2024, this review seeks examine ways that IoT-DL models contribute detection. We obtained titles, keywords, abstracts chosen papers using Scopus Web Science (WoS) databases. This study offers a comprehensive literature unresolved problems applying DL 19 were eligible be read from start finish out 2878 initially accessed. To provide practitioners, policymakers, researchers useful information, we range previous goals, approaches used, contributions those studies. Furthermore, considering numerous as they help preparedness control, structured overview current scientific trends open issues field. provides thorough state-of-the-art routing currently use, well their limits potential future developments, making it an invaluable resource practitioners tool multidisciplinary research.

Language: Английский

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Authentication Schemes for Healthcare Applications Using Wireless Medical Sensor Networks: A Survey

SN Computer Science, Journal Year: 2022, Volume and Issue: 3(5)

Published: July 18, 2022

Language: Английский

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An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works

Multimedia Systems, Journal Year: 2023, Volume and Issue: 29(3), P. 1603 - 1627

Published: March 25, 2023

Language: Английский

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A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 29, 2024

Abstract Though COVID-19 is no longer a pandemic but rather an endemic, the epidemiological situation related to SARS-CoV-2 virus developing at alarming rate, impacting every corner of world. The rapid escalation coronavirus has led scientific community engagement, continually seeking solutions ensure comfort and safety society. Understanding joint impact medical non-medical interventions on spread essential for making public health decisions that control pandemic. This paper introduces two novel hybrid machine-learning ensembles combine supervised unsupervised learning data classification regression. study utilizes publicly available outbreak potential predictive features in USA dataset, which provides information disease US, including from each 3142 US counties beginning epidemic (January 2020) until June 2021. developed hierarchical classifiers outperform single algorithms. best-achieved performance metrics task were Accuracy = 0.912, ROC-AUC 0.916, F1-score 0.916. proposed ensemble combining both allows us increase accuracy regression by 11% terms MSE, 29% area under ROC, 43% MPP metric. Thus, using approach, it possible predict number cases deaths based demographic, geographic, climatic, traffic, health, social-distancing-policy adherence, political characteristics with sufficiently high accuracy. reveals pressure most important feature analysis. Five other significant identified have influence spread. combined ensembling approach introduced this can help policymakers design prevention measures avoid or minimize threats future.

Language: Английский

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Digital Transformation Process Towards Resilient Production Systems and Networks

Springer series in supply chain management, Journal Year: 2022, Volume and Issue: unknown, P. 11 - 42

Published: Jan. 1, 2022

Language: Английский

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Performance of Machine Learning Models for Pandemic Detection Using COVID-19 Dataset

Published: Sept. 20, 2023

The pandemic produced by coronavirus2 (COVID-19) and other related infectious diseases have been confined to the world, there is a need control its spread as well prepare for any outbreak although early detection strategies. Therefore, this paper aimed identify an efficient machine learning (ML)-based model combat of diseases. Seven (7) ML-based models are studied: k-nearest neighbor (KNN), support vector machine_poly, (SVM-Poly), machine_RBF, random forest (RF), decision tree (DT), XGBoost, Logistic regression (LR) were used quick better potential COVID-19 cases. dataset utilized picks pertinent symptoms identification suspicious person from symptoms. experiments achieved XGBoost leading with accuracy 98.4%, precision 94.0%, recall 93.5%, F1-Score 94.0% respectively. results showed that real-time data capturing will efficiently detect monitor patients. This research help many teams create useful apps based on ML, DL, AI models, healthcare organizations, academics, governments demonstrating how these methods can make it easier COVID-19.

Language: Английский

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An assessment of ensemble learning approaches and single-based machine learning algorithms for the characterization of undersaturated oil viscosity

Beni-Suef University Journal of Basic and Applied Sciences, Journal Year: 2022, Volume and Issue: 11(1)

Published: Dec. 12, 2022

Abstract Background Prediction of accurate crude oil viscosity when pressure volume temperature (PVT) experimental results are not readily available has been a major challenge to the petroleum industry. This is due substantial impact an inaccurate prediction will have on production planning, reservoir management, enhanced recovery processes and choice design facilities such as tubing, pipeline pump sizes. In bid attain improved accuracy in predictions, recent research focused applying various machine learning algorithms intelligent mechanisms. this work, extensive comparative analysis between single-based techniques artificial neural network, support vector machine, decision tree linear regression, ensemble bagging, boosting voting was performed. The performance models assessed by using five evaluation measures, namely mean absolute error, relative squared root error log error. Results methods offered generally higher accuracies than techniques. addition, weak learners dataset used study (for example, SVM) were transformed into strong with better based method, while other discovered had significantly performance. Conclusion great prospects enhancing overall predictive domain fluid PVT properties (such undersaturated viscosity) prediction.

Language: Английский

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