Exploring Machine Learning Strategies in COVID-19 Prognostic Modelling: A Systematic Analysis of Diagnosis, Classification and Outcome Prediction

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Март 18, 2024

Abstract Background The COVID-19 pandemic, which has impacted over 222 countries resulting in incalcu-lable losses, necessitated innovative solutions via machine learning (ML) to tackle the problem of overburdened healthcare systems. This study consolidates research employing ML models for prognosis, evaluates prevalent and performance, provides an overview suitable features while offering recommendations experimental protocols, reproducibility integration algorithms clinical settings. Methods We conducted a review following PRISMA framework, examining utilisation prediction. Five databases were searched relevant studies up 24 January 2023, 1,824 unique articles. Rigorous selection criteria led 204 included studies. Top-performing extracted, with area under receiver operating characteristic curve (AUC) evaluation metric used performance assessment. Results systematic investigated on prognosis across automated diagnosis (18.1%), severity classification (31.9%), outcome prediction (50%). identified thirty-four five categories twenty-one distinct six categories. most chest CT, radiographs, advanced age, frequently employed CNN, XGB, RF. neural networks (ANN, MLP, DNN), distance-based methods (kNN), ensemble (XGB), regression (PLS-DA), all exhibiting high AUC values. Conclusion Machine have shown considerable promise improving diagnostic accuracy, risk stratification, Advancements techniques their complementary technologies will be essential expediting decision-making informing decisions, long-lasting implications systems globally.

Язык: Английский

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A Novel Machine Learning-Based Classification Framework for Age-Related Macular Degeneration (AMD) Diagnosis from Fundus Images

Опубликована: Май 27, 2024

Язык: Английский

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$$D_MD_RDF$$: diabetes mellitus and retinopathy detection framework using artificial intelligence and feature selection

Soft Computing, Год журнала: 2024, Номер 28(19), С. 11393 - 11420

Опубликована: Авг. 5, 2024

Abstract Diabetes mellitus is one of the most common diseases affecting patients different ages. can be controlled if diagnosed as early possible. One serious complications diabetes retina diabetic retinopathy. If not early, it lead to blindness. Our purpose propose a novel framework, named $$D_MD_RDF$$ DMDRDF , for and accurate diagnosis The framework consists two phases, detection (DMD) other retinopathy (DRD). novelty DMD phase concerned in contributions. Firstly, feature selection approach called Advanced Aquila Optimizer Feature Selection ( $$A^2OFS$$ xmlns:mml="http://www.w3.org/1998/Math/MathML">A2OFS ) introduced choose promising features diagnosing diabetes. This extracts required from results laboratory tests while ignoring useless features. Secondly, classification (CA) using five modified machine learning (ML) algorithms used. modification ML proposed automatically select parameters these Grid Search (GS) algorithm. DRD lies 7 CNNs reported concerning datasets shows that AO reports best performance metrics process with help classifiers. achieved accuracy 98.65% GS-ERTC model max-absolute scaling on “Early Stage Risk Prediction Dataset” dataset. Also, datasets, AOMobileNet considered suitable this problem outperforms CNN models 95.80% “The SUSTech-SYSU dataset”

Язык: Английский

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Optimizing Wind Power Forecasting with RNN-LSTM Models through Grid Search Cross-validation

Sustainable Computing Informatics and Systems, Год журнала: 2024, Номер unknown, С. 101054 - 101054

Опубликована: Ноя. 1, 2024

Язык: Английский

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An Aseptic Approach Towards Skin Lesion Localization and Grading using Deep Learning and Harris Hawks Optimization

Research Square (Research Square), Год журнала: 2022, Номер unknown

Опубликована: Авг. 9, 2022

Abstract Skin cancer is the most common form of cancer. Hence, lives millions people are affected by this every year. Approximately, it predicted that total number cases will double in next fifty years. It an expensive procedure to discover skin types early stages. Additionally, survival rate reduces as progresses. The current study proposes aseptic approach toward lesion detection, classification, and segmentation using deep learning a meta-heuristic optimizer called Harris Hawks Optimization Algorithm (HHO). utilized manual automatic approaches. used when dataset has no masks use while used, U-Net models, build adaptive model. HHO achieve optimization hyperparameters 5 pre-trained CNN models (i.e., VGG16, VGG19, DenseNet169, DenseNet201, MobileNet). Two collected "Melanoma Cancer Dataset 10000 Images" "Skin ISIC" dataset) from two publically available sources. For segmentation, best-reported scores 0.15908, 91.95%, 0.08864, 0.04313, 0.02072, 0.20767 terms loss, accuracy, Mean Absolute Error, Squared Logarithmic Root respectively. dataset, applied experiments, best reported overall accuracy 97.08% DenseNet169 96.06% MobileNet After computing results, suggested compared with 9 related studies.

Язык: Английский

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