Question · asked by 1 review
Adaptive dragonfly algorithm with neural-network classifier vs Support vector machine and Naive Bayes: Accuracy advantage in test case 3
- Population
- Dental images of human beings
- Intervention
- Adaptive dragonfly algorithm with neural-network classifier
- Comparison
- Support vector machine and Naive Bayes
- Outcome
- Accuracy advantage in test case 3
The review reported a 6.30% accuracy advantage in Patil test case 3 (Ungraded).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Adaptive dragonfly algorithm with neural-network classifier vs Support vector machine and Naive Bayes Accuracy advantage in test case 3 · Dental images of human beings · India, Patil et al., 2019 The review reported a 6.30% accuracy advantage in Patil test case 3 (Ungraded). Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries. (2022) All outcomes | Accuracy advantage 6.30 | 1 study · 40 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries. 2022 All outcomes | Not specified | 17 | Not applicable |
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.