Performance Comparison of Machine Learning Techniques for Breast Cancer Detection
Bibliographic record
Abstract
The fundamental cause of death among women in developed nations of the world is breast cancer. Breast cancer has been identified as one of the most deadly type of cancer prevalent among women globally. There have been a dramatic increase of breast cancer cases among women of recent. Machine learning algorithms are effective tools that have found application in the field of medical imaging for early detection and diagnosis of cancer. This paper investigate the performance of eight (8) machine learning algorithms that have been applied for timely detection of breast cancer. Diagnosing breast cancer involves making a distinction between benign and malignant breast lumps. Our experimental results indicated that Support Vector Machine (SVM) have the best performance in term of classification accuracy (97.07%) and lowest error rate compared to Radial Based Function (96.49 %), Simple Linear Logistic Regression Model (96.78%), Naive Bayes (96.48%), k-Nearest Neighbour (96.34%), AdaBoost (96.19%), Fuzzy Unordered Role Induction algorithm (96.78%) and Decision Tree - J48 (96.48%). All experiments are conducted using WEKA data mining and machine learning simulation environment. Keywords : Breast cancer; RBF, SVM; NB; AdaBoost; kNN; J48.
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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".