ArticleProceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology2018
Lung Cancer Survival Prediction via Machine Learning Regression, Classification, and Statistical Techniques.
Article in Proceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- Advancements and future trends in machine learning for lung cancer: a comprehensive bibliometric analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Opportunities and challenges in lung cancer care in the era of large language models and vision language models.Translational lung cancer research · 2025Review
- A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer.Indian journal of surgical oncology · 2025Review
- A predictive model to identify optimal candidates for surgery among patients with metastatic colorectal cancer.Frontiers in oncology · 2025Article
- Article
- Machine Learning Predict Survivals of Spinal and Pelvic Ewing's Sarcoma with the SEER Database.Global spine journal · 2024Article
- The differential prognostic implications of PD-L1 expression in the outcomes of Filipinos withTranslational lung cancer research · 2023Article
- Predicting Non-Small-Cell Lung Cancer Survival after Curative Surgery via Deep Learning of Diffusion MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Development and Validation of Novel Deep-Learning Models Using Multiple Data Types for Lung Cancer Survival.Cancers · 2022Article
- An Interpretable Two-Phase Modeling Approach for Lung Cancer Survivability Prediction.Sensors (Basel, Switzerland) · 2022Article
- Comparison of nomogram and machine-learning methods for predicting the survival of non-small cell lung cancer patients.Cancer innovation · 2022Article
- When Do We Need Massive Computations to Perform Detailed COVID-19 Simulations?Advanced theory and simulations · 2022Article
- Article
- Machine learning based prognostic model of Chinese medicine affecting the recurrence and metastasis of I-III stage colorectal cancer: A retrospective study in China.Frontiers in oncology · 2022Article
- Integration of Clinical Information and Imputed Aneuploidy Scores to Enhance Relapse Prediction in Early Stage Lung Cancer Patients.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Distinctive egg-laying patterns in terminal versus non-terminal periods in three fruit fly species.Experimental gerontology · 2021Article
- Pancreatic Cancer Survival Prediction: A Survey of the State-of-the-Art.Computational and mathematical methods in medicine · 2021Review
- Predicting long-term multicategory cause of death in patients with prostate cancer: random forest versus multinomial model.American journal of cancer research · 2020Article
- Classify multicategory outcome in patients with lung adenocarcinoma using clinical, transcriptomic and clinico-transcriptomic data: machine learning versus multinomial models.American journal of cancer research · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
A regression model is developed to predict survival time in months for lung cancer patients. It was previously shown that predictive models perform accurately for short survival times of less than 6 months; however, model accuracy is reduced when attempting to predict longer survival times. This study employs an approach for which regression models are used in combination with a classification model to predict survival time. A set of de-identified lung cancer patient data was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The models use a subset of factors selected by ANOVA. Model accuracy is measured by a confusion matrix for classification and by Root Mean Square Error (RMSE) for regression. Random Forests are used for classification, while general Linear Regression, Gradient Boosted Machines (GBM), and Random Forests are used for regression. The regression results show that RF had the best performance for survival times ≤6 and >24 months (RMSE 10.52 and 20.51, respectively), while GBM performed best for 7-24 months (RMSE 15.65). Comparison plots of the results further indicate that the regression models perform better for shorter survival times than the RMSE values are able to reflect.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.