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| 1. Verfasser: | |
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| Format: | Recurso digital |
| Sprache: | Englisch |
| Veröffentlicht: |
Zenodo
2013
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| Schlagworte: | |
| Online-Zugang: | https://doi.org/10.5281/zenodo.19005945 |
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Inhaltsangabe:
- <p>This study addresses a current research gap in Computer Science concerning AI Applications for Disease Diagnosis in Resource-Limited Healthcare Settings in Malawi in Malawi. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. AI Applications for Disease Diagnosis in Resource-Limited Healthcare Settings in Malawi, Malawi, Africa, Computer Science, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.</p>