AI-Powered Liver Cancer Detection System using Genetic Algorithm Feature Selection and Random Forest Classification for Healthcare
Bibliographic record
Abstract
Hepatocellular Carcinoma (HCC) is the most widespread type of cancer of the liver being on the rise worldwide. Early diagnosis is important in enhancing patient survival rates and the existing diagnosis mechanisms have potential of detecting the disease at late stages restricting the care available. The following paper identifies an AI-based liver cancer detection tool incorporating machine learning principles in analyzing gene expression data to diagnose HCC early in its development. The technique used in the process is a Random Forest Classifier to perform the classification exercise and a Genetic Algorithm (GA) to do the feature selection so that the most pertinent genetic markers are chosen with optimal results. The developer creates the web application via Streamlit, which has a graphic design easy to understand and manage by healthcare professionals. The system gives detailed data analytics and visualization data, which is useful in decision-making, early detection and early intervention in clinical oncology scenarios. The platform has a secure user authentication and role-based access control, as well as sensitive patient data protection. The system can change the face of liver cancer diagnostics, offering an inexpensive, scalable, and readily available solution to the entire range of healthcare settings (especially the resource-poor conditions).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".