A Machine Learning Approach for the Classification of Lower Back Pain in the Human Body
Bibliographic record
Abstract
The 21st century has been witnessing a high growth in technology in every field including the medical sector.Dynamic systems have been designed and implied for better and accurate diagnosis of a large variety of ailments; but, the growing number of patients makes it difficult to provide proper medical attention in time.To overcome this difficulty, Intelligent Systems techniques can be employed in the medical sector and help us overcome the huge difference in the ratio of doctors versus patients; along with reducing the examination and waiting time for the patients.Among all the variety of ailments prevailing in today's world, "Lower Back Pain" has emerged as one of the most prevailing ailments which includes around 80% of the total population once in lifetime, making it to one of the prior concerns of medical sector.To act effectively onto it, many conventional methods have been used to diagnose lower back pain.This study aims to design a non-Conventional technique to classify Lower back pain either Normal or Abnormal using Machine Learning techniques such as Naï ve Bayes, Support Vector Machines, Decision Trees, Gradient Boosted Trees, Fast Large Margin, K Nearest Neighbor, Multilayer Perceptron, Random Forest, and Artificial Neural Networks.This research focuses upon the implementation of the above-mentioned techniques for the proper classification of Spine Dataset and for determining the best technique in terms of Accuracy, Precision, Sensitivity, Specificity, F-measure and Area under Curve.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".