Lung Cancer Prediction and Risk Assessment: A Machine Learning Approach Integrating Symptoms and Etiological Factors
Bibliographic record
Abstract
Lung cancer requires accurate risk assessment and early detection due to its high prevalence and fatality rate. The simultaneous analysis of clinical symptoms and etiological factors using Machine Learning revolutionizes our understanding of lung cancer and patient outcomes. Machine Learning models are built on comprehensive patient data, including lifestyle, symptoms, and 12 variables. We used ten models for precise lung cancer diagnosis, including Decision Tree (DT), K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), XGBoost, Gradient Boost, CatBoost and AdaBoost. At the National Institute of Cancer Research & Hospital (NICRH) in Mohakhali, TB Gate Road, Dhaka, Bangladesh, we collected 346 data directly from patients using questionnaires, with permission from the director. For every model, we performed parameter tuning to determine the ideal ratio of complexity to accuracy. Notably, with the best accuracy of 98.43% and an excellent area under the curve (AUC) of 0.983, the Decision Tree (DT) performed better than any other classifier. The motivation behind this research is to predict the risk assignment of lung cancer using various machine learning models.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".