Integrating Survival Forests, Survival Trees, and Promotion Time Cure Models: Evidence from Breast Cancer Patients in Paraíba, Brazil

Cancer Survival Prediction through Multifactorial Analysis with Machine Learning

Risk Factor Prediction and Survival Analysis for Chronic Disease Recurrence Using Machine Learning Models

Chronic diseases are a major public health concern in KSA, reflecting global trends in prevalence and impact. Among these, ccolorectal cancer (CRC) is one that is most urgent, being the most frequently diagnosed cancer in males and the third most common in females in KSA. Our research introduces a novel approach to add...

Combining Statistical Modeling and Machine Learning for Prognostic Feature Selection in Cervical Cancer: A Retrospective Study Based on SEER 2004 to 2015 Data.

OBJECTIVES Cervical cancer is a leading female malignancy with high global morbidity/mortality, and remains high recurrence risk after standard treatment. Accurate prognostic feature identification is critical for personalized therapy and patient survival improvement, while traditional indicators and single biomarkers...

Comparison and interpretability of machine learning algorithms to predict survival of patients with prostate cancer.

OBJECTIVE To evaluate the performance and interpretability of multiple ML algorithms for survival prediction in prostate cancer (CaP) and to assess whether these approaches can improve upon established clinical risk prediction models. METHODS Using the Surveillance, Epidemiology, and End Results (SEER) 17 Database, w...

Interpretable machine learning for individualized survival prediction in node-positive, non-metastatic prostate cancer: a population-based study.

The RSF model provides accurate, robust, and interpretable prognostication for N1M0 prostate cancer, and enables precise risk stratification and individualized treatment planning as a web-based calculator.

Design and outcome-model estimability in doubly robust survival analysis: a breast cancer reanalysis

Doubly robust survival estimators require nuisance models that the available information can support. We examine design and estimator dependence in TCGA breast cancer data. Treatment indicators disagree for 337 of 1076 patients; 67.4% have unobserved five-year binary outcomes; and receptor imputation expands the triple...