Electrochemical biosensor with ML model for the early prediction of breast Cancer from urine: Preliminary outcomes
Bibliographic record
Abstract
Breast cancer is the second most frequent cancer type in the United States, with over 2.3 million cases and 685,000 reported deaths annually, according to 2024 GLOBOCAN data. Cancer detection uses diagnostic methods like magnetic resonance imaging, ultrasonography, and biopsies, which are often intrusive, expensive, painful, and have sub-optimal accuracy. Developing non-invasive, affordable, accurate, sensitive, and efficient technologies is essential for early diagnosis of breast cancer. A urine-based biosensor utilizing electrochemical analysis was developed using artificial urine as the electrolyte and screen-printed gold electrodes coated with a suitable Antibody targeting MMP-9, ALP, BCA-225, and Haptoglobin proteins. Additionally, a Support Vector Machine (SVM) and Neural Network (NN) tool-based Machine Learning (ML) model was created to assess the efficacy of breast cancer prediction. The change in impedance and capacitance that differentiates the various concentrations of the protein biomarker in artificial urine was evaluated using the Electrochemical Impedance Spectroscopy (EIS) and Cyclic Voltammetry (CV) data. Also, the ML model (SVM) and Neural Networks showed good accuracy for each protein biomarker predicting breast cancer risk when the EIS and CV data were provided. Furthermore, the outcomes of biological investigations like ELISA showed that interactions between antibodies and proteins confirmed the electrochemical findings. Our biosensor provided a more sensitive and reliable alternative to routine immunoassays. The SEM/EDX findings also supported the electrode's coating of antibodies and protein. Thus, the biosensor has the potential to become an early diagnostic tool for breast cancer and may act as a point-of-care setting using urine samples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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, 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".