The Application of A Priori Algorithms in Determining the Relationship Between Maternal Age and Pregnancy Conditions
Bibliographic record
Abstract
Pregnancy is an important phase in a woman's life that can affect the condition of the pregnancy, including the age of the mother. The age of the mother during pregnancy is often associated with certain complications, such as premature birth, preeclampsia, and fetal development disorders. Based on health data, women who are too young or too old are more likely to experience complications such as bleeding, hypertension during pregnancy, and infections during pregnancy compared to women of ideal reproductive age (20–35 years). Dr. Edward Binjai Clinic is one of the health facilities that provides services to pregnant women, including monitoring pregnancy conditions and treating complications. Until now, medical personnel at the clinic have treated patients based on experience and general protocols without a system that automatically analyzes historical patient data to find the relationship between maternal age and pregnancy risk. As a result, prevention of complications such as preeclampsia, premature birth, or pregnancy hypertension is still less than optimal. Data processing using the Apriori algorithm showed that out of 30 rules formed, there was a best rule with the highest support value of 30% and confidence of 100%. This proves that the relationship between maternal age and pregnancy conditions has a clear pattern and can be used as a basis for developing maternal health strategies, especially for vulnerable age groups.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".