Graph Convolutional Networks For Disease Mapping and Classification in Healthcare DOI
Rakesh Kumar, Devvret Verma,

J. Relin Francis Raj

et al.

Published: Dec. 29, 2023

In the context of healthcare, this study investigates use Graph A convolutional Networks (GCNs) for disease mapping along with classification. Based on an interpretivist philosophical thought, a descriptive design alongside secondary data collection is used in deductive manner. The research creates strong framework sickness mapping, assesses how well GCNs adapt to varied health information, and compares their effectiveness more conventional machine learning techniques order determine suitable they are. An investigation conducted into understanding GCN-based diagnosis models, offering valuable perspectives decision-making procedures. findings support improved diagnostic precision, wellinformed treatment planning, precision medical treatments. emphasis when applying results procedures connection systems that provide decision support, ongoing improvement. importance model interpretability, ability be general as realworld integration highlighted by critical analysis. Developing interpretability strategies addressing ethical issues are among recommendations. ensure responsible deployment, future work ought concentrate improving GCN architectures, integrating multi-modal information advocating interdisciplinary collaboration.

Language: Английский

Graph Convolutional Networks For Disease Mapping and Classification in Healthcare DOI
Rakesh Kumar, Devvret Verma,

J. Relin Francis Raj

et al.

Published: Dec. 29, 2023

In the context of healthcare, this study investigates use Graph A convolutional Networks (GCNs) for disease mapping along with classification. Based on an interpretivist philosophical thought, a descriptive design alongside secondary data collection is used in deductive manner. The research creates strong framework sickness mapping, assesses how well GCNs adapt to varied health information, and compares their effectiveness more conventional machine learning techniques order determine suitable they are. An investigation conducted into understanding GCN-based diagnosis models, offering valuable perspectives decision-making procedures. findings support improved diagnostic precision, wellinformed treatment planning, precision medical treatments. emphasis when applying results procedures connection systems that provide decision support, ongoing improvement. importance model interpretability, ability be general as realworld integration highlighted by critical analysis. Developing interpretability strategies addressing ethical issues are among recommendations. ensure responsible deployment, future work ought concentrate improving GCN architectures, integrating multi-modal information advocating interdisciplinary collaboration.

Language: Английский

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