Lifespan Prediction for Lung and Bronchus Cancer Patients via Machine Learning Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Patients' accurate survival predictions can influence treatment planning and costs, particularly lung cancer, which is one of the leading causes of cancer-related death. Machine Learning (ML) techniques are powerful in increasing the accuracy of such predictions. However, only a few studies have used an ML approach for actual lifespan prediction for cancer patients using the Surveillance, Epidemiology, and End Results (SEER) program database. This study intends to apply several well-known ML models, namely, a developed Deep Neural Networks (DNN), Linear Regression, Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Random Forest (RF), and Adaboost, to predict the actual survival time on a monthly basis for lung cancer patients. The results indicate that the models give better performance for low to average survival times (0 to 25 months) that make up the majority of the data. The best model was the developed DNN with a Root Mean Square Error (RMSE) value of 12.672. In contrast, the Adaboost model was the worst-performing technique since it had weak discrete power for the data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it