Expert System To Diagnose Pregnancy Diseases In Women Using Naive Bayes Method
Bibliographic record
Abstract
Expert system is a system that uses human knowledge, where the knowledge is entered into a computer, and then used to solve problems that usually require human expertise or expertise. In this case the expert system is used to diagnose pregnancy diseases in women. Pregnancy disease is a condition in which there is a disturbance in pregnancy or the fetus in the womb. An expert system for diagnosing pregnancy diseases in women is an expert system designed as a tool for diagnosing types of pregnancy diseases. Computer programs are intended to provide aids in solving problems in certain areas of specialization such as pregnancy problems in women. This knowledge is obtained from various sources including books and the internet related to the causes of pregnancy in women. The knowledge base is structured in such a way as to become a database with several disease tables and symptom tables to facilitate system performance in drawing conclusions on this expert system using Naive Bayes. This expert system will display a choice of symptoms that can be selected by the user, where each symptom choice will read the user to the next symptom choice to get the final result.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.009 | 0.003 |
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".