An Artificial Neural Network Approach for the Classification of Human Lower Back Pain
Bibliographic record
Abstract
In today's world, the problem of lower back pain is one of the fastest growing crucial ailments to deal with. More than half of total population on the earth, suffers from it at least once in a lifetime. Human Lower Back Pain symptoms are commonly categorized as Normal or Abnormal. In order to remedy Human Lower Back Pain, with the growth of technology over the time, many medical methods have been developed to diagnose and cure this pain at its earliest stage possible. This study aims to develop two Machine Learning (M.L.) models which can classify Human Lower Back Pain symptoms in a human body using non-conventional techniques such as Feedforward/Backpropagation Artificial Neural Networks, and Fully Connected Deep Networks. An Automatic Feature Engineering technique is implemented to extract featured data used for the classification. The proposed models are compared with respect to a Support Vector Machine model; considering different performance parameters.
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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".