[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128139-en":3,"doc-seo-128139-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128139,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Lung Cancer Survival Period Prediction - Exploring Machine Learning Approaches","Lung cancer creates the highest cancer-related burden, with 1.8 million deaths annually and the lowest average five-year survival rate of about 20%. Machine learning is applied to U.S. SEER lung cancer data to predict disease-specific survival (DSS) at 0.5-, 1-, 3-, and 5-year intervals. Supervised, ensemble, and unsupervised models show strong short-term predictive ability, while three- and five-year performance is limited by class imbalance and possibly insufficient feature capacity. Logistic regression and XGBoost are the most robust approaches, whereas K-NN and DNN perform comparatively weaker. Household income emerges as the top predictor across all time horizons, underscoring the need to address socioeconomic disparities in public health strategies.","JOE International Journal of  \nOnline and Biomedical Engineering  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJOE | eISSN: 2626-8493 | Vol. 21 No. 4 (2025) |   \n[https://doi.org/10.3991/ijoe.v21i04.52889](https://doi.org/10.3991/ijoe.v21i04.52889)  \nPAPER  \nLung Cancer Survival Period Prediction: Exploring Machine Learning Approaches  \nRooshan Ghous, Seyed Ebrahim Hosseini(􀀍), Shahbaz Pervez Chattha  \nTechnology Innovation Research Group, Whitecliffe, New Zealand  \n[seyedh@whitecliffe.ac.nz](seyedh@whitecliffe.ac.nz)  \nAll authors contributed equally to this work.  \nABSTRACT  \nLung cancer imposes the highest disease burden among all cancers and has the highest expected mortality rate, with 1.8 million deaths annually. It also has the lowest five-year survival rate, averaging at 20% among all diagnosed cancers. Machine learning (ML) offers a novel approach that has been utilized in healthcare for early detection, treatment planning, and survival time estimation. In this study, we applied various supervised, ensemble, and unsupervised ML algorithms to surveillance, epidemiology, and end results (SEER) lung cancer data to predict disease-specific survival (DSS) at 0.5-year, one-year, three-year, and five-year intervals. Our results show that ML models were effective in predicting short-term survival outcomes, but their ability to predict three-year and five-year survival was suboptimal. The limited performance of the models to predict survival outcomes may be attributed to the class imbalance that inherently exists in lung cancer patients. It may also be an indication of limited capacity of the selected features to predict long-term survival. Among the models tested, logistic regression (LR) and XGBoost were most robust algorithms to predict survival outcomes using given features. K-nearest neighbour (K-NN) and deep neural network (DNN) showed relatively weak performance as compared to other models in survival prediction. Additionally, the study found that household income, a socioeconomic factor, was the most significant predictor of survival across all time intervals. These findings highlight the potential of ML in survival prediction, particularly in the short term for lung cancer. The study also emphasizes the importance of addressing socioeconomic disparities as part of public health strategies to improve lung cancer outcomes.  \nKEYWORDS  \nartificial intelligence (AI), machine learning (ML), lung cancer, SEER data, logistic regression (LR), decision tree (DT), random forest (RF), XG Boost, support vector machines (SVM), K-nearest neighbour (K-NN), deep neural networks (DNN), socioeconomic status  \n1 INTRODUCTION  \nLung cancer is responsible for approximately 350 deaths per day, making it the leading cause of cancer-related mortality in both men and women [1] . The annual  \nGhous, R., Hosseini, S. E., Chattha, S. P. (2025) . Lung Cancer Survival Period Prediction: Exploring Machine Learning Approaches. International Journal  \nof Online and Biomedical Engineering (iJOE), 21(4), pp. 45–60. [https://doi.org/10.3991/ijoe.v21i04.52889](https://doi.org/10.3991/ijoe.v21i04.52889)[ ](https://doi.org/10.3991/ijoe.v21i04.52889)[Article submitted 2024-10-14. Revision uploaded 2025-01-10. Final acceptance 2025-01-10.](Article submitted 2024-10-14. Revision uploaded 2025-01-10. Final acceptance 2025-01-10.)© 2025 by the authors of this article. Published under CC-BY.  \niJOE | Vol. 21 No. 4 (2025) International Journal of Online and Biomedical Engineering (iJOE) 45  \nGhous et al.  \ndeath toll from lung cancer exceeds that of colon, breast, and prostate cancers combined [2] . It remains one of the most common cancers with a significantly poorer survival rate compared to other malignancies diagnosed at the same stage. To estimate the survival time of patients diagnosed with lung cancer, staging systems are widely utilized by clinicians for treatment selection and informe","cbCaiohlIUHX5iVj","https://ap.wps.com/l/cbCaiohlIUHX5iVj","pdf",714294,1,16,"English","en",105,"# Introduction\n## Lung cancer burden and survival estimation\n## Motivation for ML-based survival prediction\n# Methods\n## Dataset and prediction targets (DSS intervals)\n## Machine learning model categories\n## Evaluation considerations (class imbalance, feature capacity)\n# Results\n## Short-term vs long-term prediction performance\n## Model comparison (LR, XGBoost, K-NN, DNN)\n## Key predictors (socioeconomic factors)\n# Discussion and Implications\n## Importance of socioeconomic disparities","[{\"question\":\"Which time intervals does the study predict for lung cancer survival?\",\"answer\":\"The study predicts disease-specific survival (DSS) at 0.5-year, one-year, three-year, and five-year intervals.\"},{\"question\":\"How do machine learning models perform across short-term versus long-term survival prediction?\",\"answer\":\"ML models are effective for short-term survival outcomes, but their ability to predict three-year and five-year survival is suboptimal.\"},{\"question\":\"Which algorithms are reported as the most robust in the tested set?\",\"answer\":\"Logistic regression (LR) and XGBoost are reported as the most robust algorithms for predicting survival outcomes using the given features.\"}]","Lung Cancer Survival Period Prediction - Exploring Machine Learning Approaches | PDF",1785945031,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"lung-cancer-survival-period-prediction-exploring-machine-learning-approaches","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/lung-cancer-survival-period-prediction-exploring-machine-learning-approaches/128139/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which time intervals does the study predict for lung cancer survival?","Question",{"text":76,"@type":77},"The study predicts disease-specific survival (DSS) at 0.5-year, one-year, three-year, and five-year intervals.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do machine learning models perform across short-term versus long-term survival prediction?",{"text":81,"@type":77},"ML models are effective for short-term survival outcomes, but their ability to predict three-year and five-year survival is suboptimal.",{"name":83,"@type":74,"acceptedAnswer":84},"Which algorithms are reported as the most robust in the tested set?",{"text":85,"@type":77},"Logistic regression (LR) and XGBoost are reported as the most robust algorithms for predicting survival outcomes using the given features.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":117},"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]