Web-Based Expert System for Early Diagnosis of Skin Diseases in Cats Using the Naïve Bayes Method
Bibliographic record
Abstract
This journal discusses the development of a web-based expert system for early diagnosis of skin diseases in cats using the Naïve Bayes method. Skin disease in cats is a health problem that often occurs and requires fast and accurate diagnosis. This expert system is designed to assist cat owners and veterinarians in identifying potential causes of skin symptoms in cats. The Naïve Bayes method is used in this system because of its ability to process symptom data and produce predictions based on probability. Symptom data is collected from various sources and used to train a Naïve Bayes model. Next, the system allows users to enter symptoms observed in their cat, and the system will provide an initial diagnosis based on the information provided. The experimental results show that this expert system is able to provide an initial diagnosis of skin diseases in cats with a sufficient level of accuracy. This provides a great benefit to cat owners in taking early action and further veterinary consultation. Apart from that, this expert system can also be used as a supporting tool for veterinarians in the process of diagnosing skin diseases in cats. Thus, this research provides an important contribution to the development of expert systems in the field of animal health, especially in the early diagnosis of skin diseases in cats.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".