Feature Analysis and Classification of Inflammatory Bowel Disease and Hidradenitis Suppurativa Using Data Mining
Bibliographic record
Abstract
Inflammatory Bowel Disease (IBD) refers to a group of conditions that primarily affect the gut and cause inflammation. In contrast, Hidradenitis Suppurativa (HS) is a chronic immune-mediated condition characterized by boils in a person's underarms, groyne, and/or under their breasts. In recent years, the research on HS has been gaining a growing level of interest in light of reliable recognition of these two diseases (i.e., IBD and HS) becoming crucial in clinical settings. In this study, multiple machine learning and data mining algorithms will be investigated to shed light on HS versus IBD distinction, methods such as Decision Tree, Random Forest, Naive Bayes, and k-Nearest Neighbor algorithms. These potential solution to recognize HS-IBD boundaries are used to classify IBD and HS disease based on multiple features such as age, illness history, and clinical observations. The thesis conducts a comparative study on the various classification strategies which can be achieved through the use of machine learning in order to recognize these two diseases. These methods have been applied to the IBD/HS dataset that was collected by the medical professionals at the Mayo clinic, Rochester, MN, USA. The information consists of 198 data records and 52 attributes; however, data cleaning process was necessary before employing the machine learning. During the evaluation, the performance of approaches were compared with respect to their accuracy as the commonly used metric. Based on the findings of the conducted comparisons, it was discovered that the \\emph{random forest} approach performed the best, achieving an accuracy of (93.8%) for a reduced dataset that contained 20 features for each patient. The detailed results analysis is supported by several visualization techniques such as t-SNE. In addition, the thesis makes an effort to determine a precise set of criteria and identify the features that are the most significant in separating these two diseases from one another. The results of this study provide medical professionals with the opportunity to investigate aspects that previously were assumed to not play a significant role in clinical practice. To the best of author’s knowledge, this is the first applied study to utilize machine learning and data mining techniques for the IBD and HS classification.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".