Comparison of Colorectal Cancer Classification between K-Nearest Neighbors (K-NN) and Neural Network
Bibliographic record
Abstract
Abstract Machine learning is one of the technologies used in medicine. Machine learning can help detect various kinds of problems in the medical field and enables a process to be faster and more efficient. Cancer is one of the most dangerous diseases in the world. Machine learning is widely used in bioinformatics and particularly in cancer diagnosis. One of the most popular methods is K-nearest neighbors (K-NN) and Neural Network. There are supervised learning methods. Using K-NN, the quality of the results depends largely on the distance and the value of the parameter “k” which represents the number of the nearest neighbors. This research is explains the classification of colorectal cancer by using K-NN with different k values and Neural Network Classification. Our work will be performed on the Colorectal Cancer dataset obtained by the Al-Islam Hospital, Bandung, Indonesia and it consists of benign cases 163 and malignant cases 47 samples. Thus, the final result indicates better performance for K-nearest neighbors’ accuracy is 0.786 in K-parameter equal to 7, 9, 11 has the same accuracy with 60% data training and Neural Network reached 0.904 with 90% of data training.
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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 | 0.001 |
| 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".