Fuzzy rule based expert system to diagnose spinal cord disorders
Bibliographic record
Abstract
On time diagnosis of spinal cord problems is essential because of patient pain and cost of treatment. To alleviate this hazard, in this research a fuzzy rule-based expert system is proposed to diagnosis spinal cord problems and clarify whether a MRI scan is necessary or not. The knowledge representation of this system is provided from high level, based on lifestyle of the patient and historical data about his/her problem and some of the clinical examination. It organized to 5 sub problem, called Red flag, Spinal stenosis, Scoliosis Lordosis kyphosis, and Mechanical and Spinal disc herniation. Spinal disc herniation is composed of 2 sub problem including Lumbar and cervical. Inference engine of the system is a combination of backward and forward chaining. To reduce diagnosis time, it switches from forward to backward mode and vice versa based on direct and indirect approach. Results of this system is the degree of each problem the patient has and it reduces complexity in image processing for analysis the M.R.I of the patient.
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.
How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".