Classification of Diseases of The Urinary System using an Expert System
Bibliographic record
Abstract
Purpose – Develop and use an Expert System (ES) to classify diseases of the urinary system.Design/methodology/approach – Computational experiments were divided into three phases, as described: Phase A: Database selection: We searched for a database that contains information on diseases of the urinary system. Phase B: Development and Implementation of the Expert System: Rules and variables were planned for the correct data manipulation, and the Expert System was created by implementing the rules and variables. Phase C: Validation of the Expert System: Expert System validated by specialists.Findings – The Expert System was validated by a general practitioner and, as such, was successful while carrying out the tests and results. In conclusion, the Expert System was generated to classify two diseases (Cystitis and Nephritis) of the urinary system. This was validated by a general practitioner who confirmed the accuracy of the information within the system developed and aimed of assisting the field of medicine for a specific organ.Originality/value – The development of the present work has made it possible to assist the specific diagnosis of two diseases of the urinary system. With the assistance of the Specialist System, professionals can be more confident when diagnosing diseases of the urinary system in patients.Keywords - Urinary System; Expert Systems; Artificial Intelligence; Support to The Diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".