Application of Data Mining Using Apriori to Find Patterns of Asthma in Medical Record Data at the Health Center (Case Study: Datar City Health Center)
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
Medical records are a very important source of information in the world of health. Medical records document a patient's medical history, diagnosis, treatment, and care patterns at health facilities. However, with the large amount of data that continues to grow every day, it is often difficult for medical personnel and health facility managers to manually analyze and find useful patterns. Community health centers, as primary healthcare facilities, play an important role in addressing public health issues. Community health centers often face limitations in effectively processing available data. Therefore, methods are needed to help uncover hidden information from medical record data. One approach that can be used to analyze big data is data mining. Data mining allows users to find patterns, trends, or certain relationships that were previously unseen. In medical records, the application of data mining techniques can help identify disease patterns, relationships between diseases, and risk factors that contribute to certain diseases by using the apriori method to obtain better health service planning. From testing using the RapidMiner application, this study identified complaints, medical history, and causal factors. The results showed that there were 5 association rules formed with the highest Best rule value of 14% support and 62% confidence. The rule was “If the causal factor is genetic, the complaint is dizziness, then the medical history includes a history of asthma since childhood.”
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.003 |
| 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