Integrating Innovation in Healthcare: The Evolution of “CURA’s” AI-Driven Virtual Wards for Enhanced Diabetes and Kidney Disease Monitoring

IEEE Access, Год журнала: 2024, Номер 12, С. 126389 - 126414

Опубликована: Янв. 1, 2024

Язык: Английский

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Global Research Trends, Hotspots, Impacts, and Emergence of Artificial Intelligence and Machine Learning in Health and Medicine: A 25-Year Bibliometric Analysis

Healthcare, Год журнала: 2025, Номер 13(8), С. 892 - 892

Опубликована: Апрель 13, 2025

Background/Objectives: The increasing application of artificial intelligence (AI) and machine learning (ML) in health medicine has attracted a great deal research interest recent decades. This study aims to provide global historical picture concerning AI ML medicine. Methods: We used the Scopus database for searching extracted articles published between 2000 2024. Then, we generated information about productivity, citations, collaboration, most impactful topics, emerging author keywords using Microsoft Excel 365 VOSviewer software (version 1.6.20). Results: retrieved total 22,113 articles, with notable surge activity years. Core journals were Scientific Reports IEEE Access, core institutions included Harvard Medical School Ministry Education People’s Republic China, while countries comprised United States, India, Kingdom, Saudi Arabia. Citation trends indicated substantial growth recognition AI’s impact on Frequent identified key hotspots, including specific diseases like Alzheimer’s disease, Parkinson’s diseases, COVID-19, diabetes. keyword analysis “deep learning”, “convolutional neural network”, “classification” as dominant themes. Conclusions: transformative potential holds promise improving outcomes.

Язык: Английский

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A Review on Utilizing Data Mining Techniques for Chronic Kidney Disease Detection

Indonesian Journal of Computer Science, Год журнала: 2024, Номер 13(3)

Опубликована: Июнь 15, 2024

This comprehensive study delves into the application of machine learning (ML) and data mining techniques for prognosis diagnosis Chronic Kidney Disease (CKD), a significant global health concern characterized by gradual loss kidney function. Through detailed examination various predictive models, research evaluates efficacy different ML algorithms methodologies in classifying diagnosing CKD. Utilizing datasets from UCI repository other sources, this explores range algorithms-including logistic regression, decision trees, support vector machines, random forest, deep networks-alongside feature selection to enhance prediction accuracy facilitate early diagnosis. Despite facing challenges such as dataset limitations need external validation, findings reveal remarkable potential using improve CKD diagnosis, with some models achieving rates exceeding 99%. The underscores critical role technology advancing management, paving way more personalized effective healthcare solutions.

Язык: Английский

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Chronic Kidney Disease (CKD) Prediction Using Stochastic Deep Radial Basis for Feature Extractiona Residual Neural Network

SN Computer Science, Год журнала: 2024, Номер 5(7)

Опубликована: Окт. 15, 2024

Язык: Английский

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An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection

International Urology and Nephrology, Год журнала: 2024, Номер 56(9), С. 3133 - 3154

Опубликована: Май 15, 2024

Язык: Английский

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