Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach

Research article (Frontiers in Big Data, 2021) · cited 57× · AI/ML
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Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach

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Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach. Retrieved May 24, 2026, from https://4ort.xyz/entity/interpretability-of-machine-learning-solutions-in-public-healthcare-the-crisp-ml-approach
MLA “Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/interpretability-of-machine-learning-solutions-in-public-healthcare-the-crisp-ml-approach.
BibTeX @misc{4ortxyz_interpretability-of-machine-learning-solutions-in-public-healthcare-the-crisp-ml-approach_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach}}, year = {2026}, url = {https://4ort.xyz/entity/interpretability-of-machine-learning-solutions-in-public-healthcare-the-crisp-ml-approach}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach — https://4ort.xyz/entity/interpretability-of-machine-learning-solutions-in-public-healthcare-the-crisp-ml-approach (retrieved 2026-05-24)

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