Comprehensive overview of Alzheimer's disease utilizing Machine Learning approaches
Alzheimer's disease is a common and complex brain disorder that primarily affects the elderly. Because it is progressing and has few effective therapies, it requires a thorough understanding of the condition; our study offers a comprehensive analysis of AD with a dual approach that combines bot...
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Veröffentlicht in: | Multimedia tools and applications 2024, Vol.83 (37), p.85277-85329 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Alzheimer's disease is a common and complex brain disorder that primarily affects the elderly. Because it is progressing and has few effective therapies, it requires a thorough understanding of the condition; our study offers a comprehensive analysis of AD with a dual approach that combines both bibliometric and experimental analyses. The bibliometric analysis applies statistical and mathematical techniques to figure out the states of AD research, including publishing trends, prestigious journals, and collaborative networks. Concurrently, the experimental examination explores current advancements, focusing on Machine Learning, Deep Learning, and Metaheuristic approaches, tackling complex issues resulting from varied datasets. The experimental work is fascinating because it uses twenty classifiers and two datasets, initially without feature selection and then with seven feature selection techniques. This thorough investigation focuses on developments in disease processes, therapeutic approaches, and diagnostic tool development. This research offers a multidimensional overview of Alzheimer's disease by combining bibliometric and experimental methods, addressing problems and highlighting the shortcomings of earlier studies. By helping academics, policymakers, and healthcare professionals navigate the complexities of AD, this novel methodology advances a more thorough understanding of the Alzheimer's disease domain. |
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ISSN: | 1573-7721 1380-7501 1573-7721 |
DOI: | 10.1007/s11042-024-19425-z |