Cancer Classification From DNA Microarray Using Genetic Algorithms and Case-Based Reasoning
Bibliographic record
Abstract
There are many similarities in the symptoms of several types of cancer and that makes it sometimes difficult for the physicians to do an accurate diagnosis. In addition, it is a technical challenge to classify accurately the cancer cells in order to differentiate one type of cancer from another. The DNA microarray technique (also called the DNA chip) has been used in the past for the classification of cancer but it generates a large volume of noisy data that has many features, and is difficult to analyze directly. This paper proposes a new method, combining the genetic algorithm, case-based reasoning, and the k-nearest neighbor classifier, which improves the performance of the classification considerably. The authors have also used the well-known Mahalanobis distance of multivariate statistics as a similarity measure that improves the accuracy. A case-based classifier approach together with the genetic algorithm has never been applied before for the classification of cancer, same with the application of the Mahalanobis distance. Thus, the proposed approach is a novel method for the cancer classification. Furthermore, the results from the proposed method show considerably better performance than other algorithms. Experiments were done on several benchmark datasets such as the leukemia dataset, the lymphoma dataset, ovarian cancer dataset, and breast cancer dataset.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| grok | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
| opus | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".