Jaringan Syaraf Tiruan Memprediksi Penyakit Gerd menggunakan Metode Backpropagation
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
The rapid development of technology in the globalization era has significantly impacted various aspects of life, including the healthcare sector. RSU Bidadari Binjai, as a healthcare provider, faces challenges in diagnosing and preventing Gastroesophageal Reflux Disease (GERD), a condition with high prevalence and serious complications such as Barrett’s esophagus and esophageal cancer. Therefore, a predictive system capable of early detection is needed to ensure quicker and more effective medical intervention. This research develops a computer-based predictive system using the backpropagation method in artificial neural networks to assist in diagnosing GERD by processing patient symptom data. The system's test results show an accuracy rate of 100% in predicting GERD complications based on the given symptoms, thus supporting more timely and accurate medical interventions.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it