Expert System To Diagnose Pregnancy Diseases In Women Using Naive Bayes Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it