An Application of Traditional and Ensemble Machine Learning Approaches to Redefine Thyroid Disorder Diagnosis
Bibliographic record
Abstract
The present research explores the process of diagnosing thyroid disorders using two different methods: traditional and ensemble.The traditional method implements the ID3 decision tree algorithm on 4000 records after several preprocessing stages.This method is evaluated using various statistical approaches, which defines the proportion of true agreement between observed and predicted events.The partition accuracy for the traditional method ranges from 66% to 70%.The ensemble method relies on the Random Forest algorithm, which creates multiple trees on random datasets and integrates predictions through voting.The partition accuracy for the ensemble method, assessed using k-fold cross-validation, ranges from 68% to 74%, with a mean accuracy of 72%.The ensemble method demonstrates higher partition accuracy in diagnosing thyroid disorders compared to the traditional method.These findings suggest that ensemble machine learning techniques, particularly Random Forest, can significantly enhance diagnostic accuracy for thyroid disorders.Future research should focus on integrating these advanced algorithms into clinical diagnostic processes to improve patient outcomes and further optimize diagnostic techniques.
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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 | 0.001 | 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".