First published 10/2/2026, 11:50:15 AM · Last updated 10/2/2026, 11:50:15 AM

Naive Bayes Classification of Public Health Data with Greedy Feature Selection

Journal article published by John M. Pfau Library, California State University San Bernardino on 2014-06-19; the supplied excerpt contains no methodological details, results, dataset description, or stated impacts. Additional article content was not included in the source data received by Planet Briefing.

Illustrative image: A colorful and audaciously decorated street catfish figure in Belzoni, Mississippi, the self-proclaimed "Catfish Capital of America" — Highsmith, Carol M., 1946-/Library of Congress, Free to Use and Reuse

Categories: science-and-space, technology, public-health

Generated scores

Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.

Confidence: 3/10How certain the assessment is based on the clarity and evidence in the cited reporting.
Geographic reach: 2/10How widely the event reaches, from a local area (1) to worldwide impact (10).
Global importance: 2/10The event's broader significance, from limited importance (1) to major global importance (10).
Impact magnitude: 2/10The scale or severity of the real-world impact, from minor (1) to extreme (10).
Positivity: 5/10How harmful or beneficial the event is, from strongly harmful (1) to strongly positive (10).
Urgency: 1/10How time-sensitive the event is, from low urgency (1) to immediate attention needed (10).

Why it matters

The title indicates a technical study of classification methods applied to public health data, which may be relevant to researchers and students in data science and public health.

Location

Sources

Naive Bayes Classification of Public Health Data with Greedy Feature Selection

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